Equally
prepared
for life?
HOW 15-YEAR-OLD BOYS
AND GIRLS PERFORM
IN SCHOOL
Programme for International Student Assessment
Foreword
In the past few decades there has been an increasing interest in the different educational experiences, success
and eventual outcomes that prevail for males and females. Women often excel at school, however men
often earn more and are more likely to hold positions of power in political and economic life. Looking at
these inequalities, government policies cannot afford to be ‘gender-blind’ and must aim to develop policies
for parity. If governments wish to create growth, employment and a better standard of living, policy advice
reflecting gender differences is needed, and education could play a major role in this.
In the educational area, there are at least three reasons for studying gender differences. These include identifying
the source of inequalities, fostering average performance and improving our understanding of how students
learn.
Gender differences point to areas where student background, attitudes and characteristics significantly affect
student performance. Understanding what can influence differences in student performance can help policy
makers address quality and equity concerns. Using data from the OECD’s Programme for International Student
Assessment (PISA), this report addresses the following questions:
• Why do female and male students perform differently?
• What drives gender differences?
• Is there a need for gender-specific policies?
• Are there specific policies that would improve male or female student performance?
PISA explores the educational performance and attitudes of adolescent males and females. This report begins with
a general summary of gender differences measured independently from PISA. It then considers the knowledge
gained about gender-related issues through the PISA 2000, PISA 2003 and PISA 2006 assessments.
Some key findings include:
• In reading in PISA 2000, females significantly outscored males in all countries.
• In mathematics in PISA 2003, males often outscored females.
• In science overall in PISA 2006, there was no significant difference between males and females in the
level of performance. However, when examining the different science competencies, females were better
than males at identifying scientific issues, while males were better at explaining phenomena scientifically.
•M
ales and females did not have significantly different attitudes to to school science, but looking at their future
aspirations, there were marked differences in their expectations of having career in science at the age of 30.
The report is the product of a collaborative effort between the countries participating in PISA, the experts and
institutions working within the framework of the PISA Consortium, and the OECD. The report was drafted by
John Cresswell, Miyako Ikeda, Maciej Jakubowski, Andreas Schleicher, Sophie Vayssettes and Pablo Zoido.
The development of the report was steered by the PISA Governing Board, which is chaired by Ryo Watanabe
(Japan). The report is published on the responsibility of the Secretary-General of the OECD.
Ryo Watanabe
Chair of the PISA Governing Board
Barbara Ischinger
Director for Education, OECD
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
3
Table of contents
Foreword........................................................................................................3
Reader’s guide...............................................................................................6
Gender matters?...........................................................................................8
• Why study gender differences?......................................................................8
• Overview......................................................................................................9
What does the literature say about gender differences
From early childhood to the labour market?..................................9
• The structure of the brain..............................................................................9
• Primary education .......................................................................................9
- Reading at grade 4 (PIRLS)..........................................................................9
- Mathematics and Science at grade 4 (TIMSS)............................................10
• Secondary education .................................................................................10
- Mathematics and science at grade 8 (TIMSS)............................................10
- Secondary level attainment.......................................................................10
• Tertiary education.......................................................................................12
- Entrance into tertiary education................................................................12
- Graduation from tertiary education...........................................................13
• Labour market.............................................................................................15
What can PISA say about gender differences? ................................15
What did PISA 2000 tell us about gender differences in reading?..16
• Student performance . ................................................................................16
• Trends in gender differences in reading between PISA 2000
and PISA 2006............................................................................................17
• Student interest and engagement in reading................................................17
• Other gender differences found in PISA 2000.............................................18
What did PISA 2003 tell us about gender differences,
in mathematics?...........................................................................................19
• Student performance...................................................................................19
• Trends in gender differences in mathematics between PISA 2003
and PISA 2006............................................................................................20
• Student attitudes.........................................................................................20
What did PISA 2003 tell us about gender differences in problem
solving?.........................................................................................................21
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
What did PISA 2006 tell us about gender differences
in science?....................................................................................................22
• The PISA science framework.......................................................................22
• Student performance...................................................................................23
- Overall performance in science................................................................23
- Performance in the competency - identifying scientific issues...................24
- Performance in the competency - explaining phenomena scientifically....27
- Performance in knowledge about science.................................................32
- Performance in the “Physical systems” area of knowledge of science ......32
- Performance in the “Living systems” area of knowledge of science...........33
- Performance in the “Earth and space systems” area of knowledge of science .33
• Gender differences within schools .............................................................33
• A typology of gender differences in science................................................34
• Computer-based assessment of science (CBAS) in PISA 2006......................34
• Student attitudes.........................................................................................36
- Science performance and attitudes towards science.................................37
• Student background....................................................................................38
- Socio-economic background....................................................................38
- Immigrant status.......................................................................................39
- Parental involvement................................................................................41
• Future career orientation . ..........................................................................41
- Information on science-related careers and preparation for the future.......41
- Do students expect to pursue a scientific career?......................................43
• School organisation ...................................................................................44
- Single sex-schooling.................................................................................44
- Homework...............................................................................................47
Conclusion...................................................................................................47
REFERENCES.....................................................................................................50
APPENDIX a - BACKGROUND OF PISA...........................................................52
• The development of PISA surveys................................................................52
• The PISA student population.......................................................................52
• Key features of PISA 2006...........................................................................52
• The PISA 2006 science assessment framework............................................54
• Development of the science items in PISA 2006.........................................55
APPENDIX B - DATA TABLES...........................................................................56
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
5
Reader’s Guide
Data underlying the figures
The data referred to in this report are presented in Appendix B and, with additional detail, on the PISA
website (www.pisa.oecd.org). Five symbols are used to denote missing data:
a The category does not apply in the country concerned. Data are therefore missing.
cThere are too few observations to provide reliable estimates (i.e. there are fewer than 30 students
or less than 3% of students for this cell, or too few schools for valid inferences).
mData are not available or have been removed for technical reasons.
wData have been withdrawn at the request of the country concerned.
xData are included in another category or column of the table.
Calculation of the OECD average
An OECD average was calculated for most indicators presented in this report. The OECD average
corresponds to the arithmetic mean of the respective country estimates.
Rounding of figures
Because of rounding, some figures in tables may not exactly add up to the totals. Totals, differences
and averages are always calculated on the basis of exact numbers and are rounded only after
calculation.
All standard errors in this publication have been rounded to two decimal places. Where the value
0.00 is shown, this does not imply that the standard error is zero, but that it is smaller than 0.005.
Reporting of student data
The report uses “15-year-olds” as shorthand for the PISA target population. PISA covers students who
are aged between 15 years 3 months and 16 years 2 months at the time of assessment and who have
completed at least 6 years of formal schooling, regardless of the type of institution in which they are
enrolled and of whether they are in full-time or part-time education, of whether they attend academic
or vocational programmes, and of whether they attend public or private schools or foreign schools
within the country.
Reporting of school data
The principals of the schools in which students were assessed provided information on their schools’
characteristics by completing a school questionnaire. Where responses from school principals are
presented in this publication, they are weighted so that they are proportionate to the number of 15year-olds enrolled in the school.
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Reporting of parent data
The parents of the students who were assessed provided information on their perception of their son’s
or daughter’s schools and on the activities undertaken by their children at the age of 10, by completing
a parent questionnaire.
Abbreviations used in this report
The following abbreviations are used in this report:
ISCEDInternational Standard Classification of Education
ISCOInternational Standard Classification of Occupations
S.D.Standard deviation
S.E.Standard error
Significance tests and subgroup comparisons
The significant statistics in this report have been highlighted in the figures and tables, using darker
tone and bold print respectively. For further information, see the Annex A3 in PISA 2006: Science
Competencies for Tomorrow’s World (OECD, 2007a).
Further documentation
For further information on the PISA assessment instruments and the methods used in PISA, see the
PISA 2006 Technical Report (OECD, 2009b) and the PISA website (www.pisa.oecd.org).
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
7
Gender matters?
For the past few decades there has been an increasing interest in the different educational experiences,
success and eventual outcomes that prevail for males and for females. The interest in this area was fuelled
in part by a perceived lack of interest and success of females in a number of areas of schooling – notably
mathematics and the physical sciences. In more recent times there has also been a focus on the lack of
engagement and success of males, especially in the area of reading.
Educational policy has to take into account the existence of gender differences in performance to be effective
in promoting quality student outcomes and equity. This report draws heavily on the OECD’s Programme for
International Assessment (PISA) where it has been found that female students do better in reading (OECD,
2001) and male students do somewhat better in mathematics (OECD, 2004). In science, the picture is more
complex. It has been found that student attitudes and engagement explain, in part, gender difference in
mathematics and reading, a finding that, by itself, can foster a better understanding of how students learn
and thereby help design more effective educational policies (OECD, 2007a).
This report will look at the development of gender related issues during the years of childhood and
adolescence. The report will:
• consider briefly gender differences measured outside the PISA assessment programme;
• review the knowledge gained about gender related issues from PISA 2000 and PISA 2003 when reading
and mathematics respectively were the major domains of assessment, with attention also paid to the
relationship between student performance and student attitudes;
• consider in more detail the results from the most recent PISA survey, PISA 2006, when science was the
major assessment domain, considering also student attitudes to science and the environment.
Why study gender differences?
There are at least three reasons to study gender differences: i) to understand the source of any inequalities;
ii) to improve average performance; and iii) to improve our understanding of how students learn. Gender
differences point to areas where student background and characteristics significantly affect student
performance. Understanding what drives differential student performance can foster the design of effective
educational policies to address quality and equity concerns. Why do female and male students perform
differently? What drives gender differences? Is there a need for gender specific policies? Are there specific
policies that would improve male or female student performance? These are some of the questions that can
be analysed by looking into gender differences.
The imperative for gender equity can be seen in a number of lights. Firstly there is a moral reason to ensure
that one of the sexes is not disadvantaged compared to the other. The disadvantage may be the end result
of many years of treatment based on culture, religion and tradition. The second imperative to raising the
performance of one of the sexes to be similar to the other is the concomitant increase in economic and
social benefits that this will bring. Belfield and Levin (2007) calculated the costs and benefits associated
with an increase in education level (as measured by high school graduation). They found that there are
both private benefits to the individual who graduates and fiscal benefits to the taxpayer through higher tax
revenues because of increased earnings and lower government expenditures on health, crime, welfare,
remedial education and other public services.
The countries with the smallest number of gender differences in performances and attitudes (Table 3.21,
OECD, 2007a) include Portugal, Poland, Belgium, Switzerland, Ireland, Mexico, the Slovak Republic and
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Spain. In these countries it may be that efforts to improve student performance need to be targeted at both
males and females, whereas in countries with a larger number of gender differences – for example the
Netherlands, Iceland and Norway – it may be more useful to focus on one of the sexes.
Overview
This report begins with a short summary of gender differences from early childhood through to labour
market participation. A discussion of how PISA can inform the consideration of gender differences follows.
A brief section reviews gender differences in reading from PISA 2000 and in mathematics and problem
solving from PISA 2003. A discussion of science in PISA 2006 follows, including information derived from
the computer based assessment of science in PISA 2006. This section also includes discussion of parents’
perceptions, and of the relationships between performance and socio-economic background, single-sex and
mixed-sex schooling, students’ attitudes to school and the amount of time that they spend on homework.
The report also looks at trends in some of these relationships over time.
In summary, the report concludes that there are significant gender differences in educational outcomes, and
these appear as students grow older and gain education and labour market experience. Despite differences
in the structure of the brains of females and males, there is no conclusive evidence that these differences
lead to later differences in educational outcomes. There is some evidence of gender difference in early
educational experiences but they are confined to reading. As students progress in their education, however,
gender differences become more pronounced. In addition, labour market outcomes show significant earning
gaps in favour of males.
What does the literature say about gender differences From early
childhood to the labour market?
The structure of the brain
In recent years there has been much interest in investigating potential links between the structure of the brain
and differing educational outcomes for males and females. The OECD report, Understanding the Brain: The
Birth of a Learning Science (OECD, 2007b), synthesised progress on the brain informed approach to learning
(that is a detailed consideration of the relationship between the structure of the brain and a child’s capacity
and approach to learning) and addressed a number of key educational issues. There are, indeed, functional
and morphological differences between the male and female brain. The male brain is larger, for instance,
but when it comes to language, the relevant areas of the brain are more strongly activated in females.
Determining the importance of these differences in structure is extremely difficult. No study to date has
shown gender-specific processes involved in building up the networks in the brain during learning.
Primary education
Small gender differences appear at early stages of education. Universal primary education is widespread
in most countries and few gender differences appear in attainment, that is, in terms of the proportions of
males and females completing primary education. In terms of performance, on the other hand, international
assessments of primary school students show significant gender differences in reading in favour of females.
On the other hand, there are few gender differences apparent in mathematics and science.
Reading at grade 4 (PIRLS)
The latest cycle of the Progress in International Reading Literacy Study (PIRLS) conducted by the International
Association for the Evaluation of Educational Achievement (IEA) took place in 40 countries in 2006 (including
19 OECD countries and 10 non-OECD countries and economies which also participated in PISA 2006). The
target population was fourth-grade students.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
9
PIRLS found that females had significantly higher reading achievement than males in all except two countries,
Luxembourg and Spain, where average achievement for males and females was equal. On average across
the participating countries females scored 17 score points more than males in a test where the mean score
was 500 and the standard deviation 100. In the OECD countries that participated the difference in favour
of females was highest in New Zealand (24 score points). In terms of behaviour, PIRLS found that females
reported more time than males reading books or magazines (1.5 hours vs. 1.3 hours), a difference being
found in almost all participating countries. In comparison, on average, males reported more time than
females reading on the Internet (1.0 hours vs. 0.9 hours) (Mullis, Martin, Kennedy and Foy, 2007).
Mathematics and science at grade 4 (TIMSS)
The IEA also conducts an assessment of student capacity in mathematics and science at grade 4 – the
Trends in International Mathematics and Science Study (TIMSS). There were 36 countries and economies
participating in TIMSS 2007. At grade 4 level, the TIMSS 2007 data show that on average in the participating
countries there was not a gender difference in overall mathematics performance. Females had significantly
higher scores in eight countries, while males had significantly higher scores in twelve countries (Mullis,
Martin & Foy, 2008).
In science, at the fourth grade, performance for females was slightly higher than for males across the
participating countries (3 points), although the situation varied from country to country. In more than
half the countries (22), the difference in average achievement in science between females and males was
negligible at the fourth grade. Males had higher average science achievement than females in 8 countries
(Martin, Mullis & Foy, 2008).
Secondary education
Mathematics and science at grade 8 (TIMSS)
The second component of TIMSS is a study of the mathematics and science achievement of students at grade 8.
At the eighth grade, on average across the TIMSS 2007 countries, females had higher average achievement
than males in mathematics. Females had higher achievement than males in 16 of the participating countries,
while males had higher achievement than females in 8 countries (Mullis, Martin & Foy, 2008).
In science, at the eighth grade, on average across the TIMSS 2007 countries, females had higher average
achievement than males by 6 points. Females had higher achievement than males in 14 of the participating
countries. Males had higher achievement than females in 11 countries (Martin, Mullis & Foy, 2008).
Secondary level attainment
Graduation rates for female students are generally higher than for males. The 2008 edition of the OECD
report Education at a Glance 2008 – OECD Indicators (OECD, 2008) gives a detailed description of
education systems in all OECD member countries and some partner countries (Brazil, Chile, Estonia, Israel,
the Russian Federation and Slovenia). Data on gender differences in secondary education cover a number
of important areas including overall attainment and graduation rates in different programmes. In general, the
data show a gap in favour of females in terms of graduation rates, especially in general programmes.
In Table 1 it can be seen that across the OECD in 2006 there was an average of 79% of males and 87% of
females who were upper secondary graduates. However, within some countries the differences between
males and females were marked. The largest differences in favour of females were observed in New Zealand
(23%), Norway (23%) and Iceland (19%). The country with the biggest difference in favour of males was
Turkey (7%). Comparing the 2006 data with the figures for 2000 shows that there has been a slight overall
10
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Figure 1
Graduation rate at upper secondary level for general programmes, by gender (2006)
%
90
g
Males
g
Females
80
70
60
50
40
30
20
10
Austria
Czech Republic
Luxembourg
Source: Education at a Glance 2008 - OECD Indicators (OECD, 2008).
Slovak Republic
Turkey
Switzerland
Netherlands
Italy
Sweden
Mexico
Chile
Belgium
Slovenia
Portugal
Germany
Spain
OECD average
France
Israel
Finland
Ireland
Korea
Denmark
Poland
Norway
Brazil
Estonia
Japan
Greece
Iceland
Australia
Canada
Hungary
0
Figure 2
Graduation rate at upper secondary level for vocational and pre-vocational programmes,
by gender (2006)
%
�
Males
�
Females
120
100
80
60
40
20
Mexico
Brazil
Canada
Estonia
Portugal
Hungary
Japan
Turkey
Poland
Israel
Korea
Greece
Chile
Spain
Austria
Sweden
Norway
Luxembourg
OECD average
Australia
Iceland
Denmark
Germany
France
Belgium
Italy
Switzerland
Netherlands
Slovak Republic
Czech Republic
Ireland
Slovenia
Finland
0
Source: Education at a Glance 2008 - OECD Indicators (OECD, 2008).
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
11
increase in the percentage of upper secondary school graduates, but the overall difference between
percentages of males and females is little changed, the figures for 2000 having been 74% of males and 80%
of females (OECD, 2000).
It is possible to further analyse upper secondary graduation rates into graduation rates for general
programmes and for pre-vocational and vocational programmes. Figure 1 shows that graduation rates for
general programmes are higher for females in all countries. The largest difference in the OECD countries
is in Norway where 44% of males compared to 68% of females graduate. Across the OECD the average is
41% for males and 53% for females. In the partner countries the largest difference is in Estonia (45% for
males and 72% for females).
Looking at graduation rates for pre-vocational and vocational programmes, Figure 2 shows a different
pattern, with around two-thirds of the countries having a greater percentage of males graduating. In Austria
there are 23% more males than females graduating in pre-vocational and vocational programmes. In Poland
this figure is 18% and in Italy 14%. Among the countries where females predominate in pre-vocational and
vocational programmes, Ireland has the largest difference with 33% more females, followed by Finland
(17%) and Denmark (10%).
Tertiary education
In tertiary education, females have been narrowing traditional gaps with male students. In mathematics
and computer science however, graduation rates for females are still lower than the graduation rates for
males. In today’s global economy, where education and human capital accumulation drive innovation and
competitive advantage, increasing graduation rates among female students is for many countries the most
immediately available opportunity for increasing the output of graduates in these critical areas.
As the more detailed analysis below indicates, while the number of female students in tertiary education
has increased more rapidly than that of males, the proportion of women choosing science and technology
studies is still lower than that of men. Furthermore, even though the share has often increased in countries
which had the lowest proportions of female science and technology students, trend analysis suggests that
the overall proportion of female science and technology graduates tends to level out at around 40%.
Entrance into tertiary education
Since PISA focused on science in 2006, it is relevant to look at the entry of students into tertiary education
and, in particular, to examine the taking up of various science based courses (life sciences, physical sciences,
computing, and mathematics and statistics). It can be seen (Figure 3) that females dominate in the life
sciences in all countries. The highest percentage is in Slovenia where 76% of students in life science courses
are female. At the other extreme the numbers of females taking up computing courses remain much lower
than males. In Belgium only 7% of entrants to computing courses are female, with the highest proportion of
female entrants being 34% in Mexico.
The OECD’s Global Science Forum in its policy report, Evolution of Student Interest in Science and
Technology Studies (OECD, 2006b), found that women were strongly under-represented in science and
technology studies. This was considered to be important because, with the number of males at a level
where a large increase is not likely, female students are the most obvious resource for increasing science
and technology enrolments.
The choice of discipline appears to be highly gender-dependent. In most countries, women constitute less
than 25% of computing and engineering students. In contrast, women are consistently more numerous than
men in life sciences.
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Figure 3
Proportion of females in new entrants at tertiary level, by field of education
This is the sub-title of the figure and it is better to build them inside the layout instead of keeping it with the chart
it-self. better when making the corrections
%
�
Life sciences
�
Physical sciences
�
Mathematics and statistics
�
Computing
90
80
70
60
50
40
30
20
10
Korea
Switzerland
Chile
Turkey
Mexico
Australia
Belgium
United Kingdom
Source: Education at a Glance 2008 - OECD Indicators (OECD, 2008).
Netherlands
Sweden
Hungary
Poland
Denmark
New Zealand
OECD average
Israel
Spain
Austria
Norway
Italy
Portugal
Germany
Czech Republic
Ireland
Estonia
Finland
Iceland
Slovenia
0
The report also concluded that there were significant differences between males and females in their attitudes
to science. The Global Science Forum reported that it appeared young female students may suffer from
stereotypes in relation to external expectations (those of parents, teachers and society in general) because
despite having marks at least as good as males, females are usually not encouraged to pursue science and
technology career paths by their families, teachers and career advisors. Females tended to undervalue their
own performance, and hence their ability to pursue science and technology. There also may be a lack of
role models for females (famous scientists, family members, etc.). Students’ attitudes to careers are formed
at secondary school and PISA provides a great deal of information on this which is discussed later in the
report.
Graduation from tertiary education
As Figure 4 shows, in nearly all OECD countries the percentage of females graduating for the first time is
larger than the percentage of males (OECD, 2008). Furthermore, as Figure 5 shows, on average in OECD
countries, more than 70% of the tertiary graduates in the humanities, arts, education, and health and
welfare are females, whereas only around 25% of those graduating in mathematics and computer science
or engineering are females.
Figure 5 also shows that the percentage of graduates who are female declines as the level of education
increases (OECD, 2008). The proportions of first or second tertiary–type A degree graduates in 2006 who
were female were 58% and 56% respectively, and only 43% of advanced research qualifications, the highest
level of education, were awarded to females. In OECD countries, the gap between males and females at
this highest level of education has decreased between 2000 and 2006 – in 2000 39% of the graduates were
female.
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13
Figure 4
Tertiary-type A graduation rates, by gender (2006)
build them inside
the layout instead of keeping it with the chart
Males
g Females
% This is the sub-title of the figure and it is bettergto
it-self. better when making the corrections
100
90
80
70
60
50
40
30
20
10
Turkey
Austria
Germany
Greece
Slovenia
Switzerland
Japan
Czech Republic
Spain
Hungary
United States
Slovak Republic
Israel
Canada
Portugal
United Kingdom
Italy
OECD average
Ireland
Netherlands
Sweden
Norway
Finland
Denmark
Poland
Australia
New Zealand
Iceland
0
Source: Education at a Glance 2008 - OECD Indicators (OECD, 2008).
Figure 5
Percentage of tertiary-type A qualifications awarded to females and breakdown
of tertiary graduates by field of education, OECD average (2000, 2006)
This is the sub-title of the figure
and it isofbetter
to buildAthem
inside theawarded
layout instead
of keeping
� Percentage
tertiary-type
qualifications
to females
2000 it with the chart
it-self. better when making the corrections
� Percentage of tertiary-type A qualifications awarded to females 2006
� Percentage of tertiary-type A graduates 2000 � Percentage of tertiary-type A graduates 2006
%
80
70
60
50
40
30
20
10
Engineering, manufacturing
and construction
Social sciences, business,
law and services
Humanities, arts
and education
Mathematics and
computer science
Life sciences, physical sciences
& agriculture
Health and welfare
Advanced research
degree
Second tertiary-type A
degrees
First tertiary-type A
degrees
0
Source: Education at a Glance 2008 - OECD Indicators (OECD, 2008).
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The United States may provide an example of what is happening in a number of OECD countries. In
examining the physical sciences in the US, Taasoobshirazi and Carr (2008) wrote that the underrepresentation
of women in the sciences is a significant and well documented societal concern and they cite Miller (2006)
and Stake (2006). Using figures from the National Science Foundation, they found that in recent years,
women had received 34% of the master’s degrees in computer science, 21% of the master’s degrees in
physics, 41% of the master’s degrees in chemistry, and 21% of the master’s degrees in engineering. Results
for doctoral degrees were similar: women received 19% of the doctoral degrees in computer science, 13%
in physics, 32% in chemistry, and 17% in engineering. Thus, they concluded that women were greatly
underrepresented in more advanced science degrees and degrees involving physics and engineering.
In relation to attitudes to science, the Global Science Forum report (OECD, 2006b) noted that “many
surveys have shown clear differences between males and females in their experience with, interest in, and
attitudes to science and engineering, so it is not surprising to see these attitudes transposed into differences
in their choice of studies. Furthermore, females tend to show a stronger interest in people rather than facts
or things, and these differences may be amplified in the way science and technology are taught, and in the
perception of science and technology careers. These differences do not appear to be related to ability, since
females tend to succeed well in science and technology, especially in the early stages. Some experts are
working on the re-engineering of the education process to offer equal opportunity to both genders, but no
consensus has yet emerged concerning the assumptions, methods, or results that can be achieved.”
Labour market
Gender differences are apparent in labour market outcomes. In many countries the earnings of males and
females are different for all levels of educational attainment. With few exceptions, females earn less than
males with similar levels of educational attainment. For all levels of education, average earnings of females
between the ages of 30 and 44 range from 51% of those of males in Korea to 89% in Slovenia (OECD,
2008, p.163).
Although the gap in earnings between males and females is explained in part by factors other than education
(that is by different choices of career and occupation, by differences in the amount of time that males and
females spend in the labour force, and by the relatively high incidence of part-time work among females),
it is appropriate to examine these differences further, including using data from PISA 2006 to look at the
attitudes of students to science before they enter the workforce.
What can PISA say about gender differences?
PISA is well placed to further our understanding of the emerging patterns of gender differences developing
through childhood and adolescence suggested by the evidence presented above. Because of its widespread
implementation and the nature of the assessment, PISA can provide a great deal of information about gender
differences in student performance and attitudes at the secondary level. These measurements are made at
an important and formative time of an adolescent’s life and can provide some insight into the patterns of
behaviour that may develop.1
PISA seeks to provide information about the key competencies of 15-year-old students and is administered
every three years in OECD member countries and a group of partner countries and economies. It assesses
the extent to which students near the end of compulsory education have acquired the knowledge and skills
that are needed for participation in society, focusing on student competencies in the key areas of reading,
mathematics and science. PISA seeks to assess how well students can extrapolate from what they have
learned at school and apply their knowledge in novel settings.
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PISA can be used to give information about the performance of education systems, the equity in distribution
of learning opportunities, consistency of school performance within a country and student dispositions to
learning and to issues in the community. Performance in PISA is reported both in terms of a score (with
mean of 500 and a standard deviation of 100 across OECD countries) and also as a proficiency level,
which describes the tasks that a student can do. In science there are six proficiency levels, with Level 6
representing the highest capacity.
This report focuses on the gender differences observed in the first three PISA surveys. In PISA 2000 reading
was the major area of assessment, in 2003 it was mathematics and in 2006 it was science. There are sections
below devoted to each of these areas of assessment, with a more detailed description of the most recent
survey in 2006 which focused on students’ competency in science, offering a comprehensive international
measurement in this area. In today’s technology-based societies, understanding fundamental scientific
concepts and theories and the ability to structure and solve scientific problems are more important than
ever. PISA 2006 assessed not only science knowledge and skills, but also the attitudes which students had
towards science.
What did PISA 2000 tell us about gender differences in reading?
PISA 2000 focused on reading and studied gender differences in great detail. One of the main findings of
the initial PISA 2000 report was that females outperform males in reading and in all of its subcomponents.
It was also the case that females showed a lot more interest than males in reading and in part this explains
the performance gap.
Student performance
The study of gender differences has always been one of the main areas of interest to countries participating
in PISA. In the first cycle of PISA in 2000 the main focus of assessment was reading and a chapter of the
initial report, Knowledge and Skills for Life: First results from PISA 2000 (OECD, 2001), was devoted to
gender differences. There were some large gender differences observed in both student performance and
student attitudes.
In all participating countries females significantly outperformed males. The advantage to females, on
average across the OECD, was 32 score points. This is nearly one half of a proficiency level (there are
five proficiency levels in reading). There was, however, significant variation between countries in the size
of gender differences. The largest difference between males and females was in Finland (the top-scoring
country overall) with a difference of 51 score points. It must be pointed out, however, that males in Finland
did not score poorly in PISA – there is no other country where males scored more highly – it is just that the
females obtained exceptionally high results.
The distribution of performance showed strong contrasts between females and males. In the PISA 2000
assessment 11.9% of females performed at the highest level of proficiency (Level 5) compared with 7.2%
of males. In all OECD countries males were more likely than females to be among the lowest-performing
students. At Level 1 and below on the combined reading literacy scale, the ratio of males to females ranged
from 1.3 to 3.5.
In PISA 2000 three areas of reading competency were measured on the retrieving information scale, the
interpretation scale and the reflection and evaluation scale. With few exceptions, females outperformed
males on every scale, with gender differences strongest in reflection and evaluation. On average, gender
differences were 45 score points in favour of females on the reflection and evaluation scale, compared with
29 score points on the interpretation scale and 24 score points on the retrieving information scale.
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Trends in gender differences in reading between PISA 2000 and PISA 2006
Since 2000 there has been a great deal of interest in whether any changes have occurred in reading
performance. Overall, performance in reading declined slightly (though not statistically significantly)
between 2000 and 2006 - by 6 score points on average in the countries where valid comparisons could be
made (Table 6.3a, OECD, 2007a).2 At the same time the difference between males and females increased
from 32 to 38 score points.
This increased difference is largely due to the fact that between 2000 and 2006 the performance of males
decreased (statistically significantly) by 10 score points. The largest decline in an OECD country was in
Spain with a 38 score point decrease. There were also statistically significant decreases in the performances
of males in eight other OECD countries – Iceland (28 score points), Japan (25 score points), Greece and
Norway (24 score points), Italy (22 score points), France (21 score points), Mexico and Australia (18 score
points).
Among the partner countries the largest decline in male scores was in Argentina (48 score points) with
statistically significant declines also in Romania (47 score points), Bulgaria (34 score points), the Russian
Federation (23 score points) and Thailand (21 score points).
In terms of the distribution of performance, the proportions of males and females performing at the highest
level in 2006 (6% of males and 11% of females) were very similar to those in 2000 (7% of males and 12%
of females). For the students who were at the lower proficiency levels (i.e. Level 1 and below) in 2006 there
were 26% of males and 14% of females, compared to the results of PISA 2000 where there were 22% of
males and 13% of females.
Student interest and engagement in reading
In PISA 2000 students were also asked questions about their interest in reading, their reading habits, what
sort of material they read and how long they read for. The responses from these questions were combined
to construct a number of indices about students’ interest and engagement in reading. These indices revealed
some significant differences between males and females. PISA 2000 found that students’ interest and
engagement was associated with a significant portion of the gender differences in performance.
The PISA index of interest in reading was derived from students’ level of agreement with the following
statements: i) I read because reading is fun, I wouldn’t want to give it up; ii) I read in my spare time; and iii)
when I read, I sometimes get totally absorbed. A four-point scale with the response categories “disagree”,
“disagree somewhat”, “agree somewhat” and “agree” was used.
As for many of the indices discussed below, the average score across the OECD on the PISA index of interest
in reading was standardised at 0 with a standard deviation of 1. Across the OECD males had an average
score of -0.24 while females had an average score of 0.26 (i.e. half of a standard deviation difference). In
all countries males expressed less interest in reading than females. More generally, however, about half of
the students surveyed in PISA 2000 were positive about reading with 48% of them agreeing or agreeing
somewhat that reading is fun and that they would not want to give it up.
While it is not possible to make a causal link between student interest and performance because many
factors are involved, the evidence in PISA 2000 suggests that they are associated. One standard deviation
difference in the index of student interest is associated with a change of 27.9 score points on the PISA
reading scale.
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Students were also asked about their habits regarding reading. The PISA index of engagement in reading was
derived from students’ level of agreement with the following statements: i) I read only if I have to; ii) reading
is one of my favourite hobbies; iii) I like talking about books with other people; iv) I find it hard to finish
books; v) I feel happy if I receive a book as a present; vi) for me reading is a waste of time; vii) I enjoy going
to a bookstore or a library; viii) I read only to get information that I need; and ix) I cannot sit still and read
for more than a few minutes. A four-point scale with the response categories “strongly disagree”, “disagree”,
“agree” and “strongly agree” was used.
Across the OECD in 2000, 46% of males read only if they had to, compared with 26% of females. In
addition, 58% of males read only to obtain information that they needed, as opposed to 33% of females.
Similarly, 25% of males reported that reading is one of their favourite hobbies, compared with 45% of
females. Males also tend to spend much less time reading than females, with 30% of them, on average,
reporting that they read for enjoyment for more than 30 minutes each day, compared to 45% of females.
Following the production of the PISA initial reports for each cycle of PISA, further focused reports are also
produced. In the thematic report Reading for Change - Performance and Engagement across Countries:
Results from PISA 2000 (OECD, 2002) students were categorised into different clusters according to their
reading habits – there were four types of students described: i) least diversified readers; ii) moderately
diversified readers; iii) diversified readers in short texts; and iv) diversified readers in long texts.
As shown in table 3, the classification highlights a clear difference in the reading patterns of males and
females. Roughly equal proportions of males and females fell into each of the first two groups. However,
34% of males fell into the “diversified readers in short texts” group, compared with 23% of females, while
conversely 29% of females, compared with 16% of males, fell in the “diversified readers in long texts”
group, where students identify themselves as readers of newspapers, magazines, books (especially fiction),
but not comics.
Other gender differences found in PISA 2000
PISA 2000 investigated a number of other student characteristics including students’ sense of belonging to
school and students’ level of participation at school, i.e., their level of participation in school activities. The
thematic report Student Engagement At School, a Sense Of Belonging and Participation: Results from PISA
2000 (Willms, 2003) found a similar sense of belonging for females and males. It is possible to express a
comparison between two measures using odds ratios, which express the likelihood of an event occurring in
one group of the population compared to the likelihood of the same event occuring in another group – if the
value is 1.0 then the both events are equally likely to occur. On average, the odds of a female having a low
sense of belonging were 0.98 that of a male, not statistically significantly different, indicating that males and
females were equally likely to have a low sense of belonging. However, the odds ratio of females having a
low sense of participation in school was 0.93, which suggested that the likelihood of a female having low
participation was about 7% less than that of a male. This difference was statistically significant.
PISA 2000 also showed differences between males and females in their preferred modes of learning. These
differences were described in the thematic report Learners For Life: Student Approaches To Learning: Results
From PISA 2000 (Artelt et al, 2003). The main findings of the report were:
• In general, when approaching a learning task, females were better at working out what knowledge and
skills they needed to know to solve the problem, whereas males were better at processing information.
• Motivation showed contrasting gender differences. In most countries females expressed significantly
greater reading interest and claimed more effort and persistence. On the other hand, males showed
significantly more interest in mathematics in most countries.
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• Students’ self-related beliefs showed similar patterns, with females generally confident in their verbal
abilities and males in their mathematical abilities. A particular advantage for male students, their biggest
strength outside the mathematics domain, was their confidence in being able to succeed in tasks, even
where they found them difficult. Even though the extent of this advantage was modest, its incidence was
widespread: it was identifiable at a statistically significant level in all but three OECD countries.
• Males and females have different preferences in the way that they learn with their peers: in most countries
males were more likely than females to be positive about competitive learning situations. In about half
of countries, females were more likely than males to say that they like learning co-operatively; in the rest
there was no difference, except in Korea where males favoured co-operative learning more.
In summary the results of PISA 2000 show clearly an advantage to females in reading, not only in the
performance on the assessment, but also in the attitudes and engagement that females have to reading.
What did PISA 2003 tell us about gender differences in mathematics?
The broader gender patterns in later career and occupational choices are already apparent in the mathematics
performance of 15-year-old males and females as observed by PISA. Gender patterns in mathematics
performance are fairly consistent across OECD countries (Learning for Tomorrow’s World – First Results from
PISA 2003). In most countries male students outperformed female students in the combined mathematics
scale and every subscale. In terms of attitudes, the study found even greater gender differences. Female
students consistently reported lower levels of enjoyment, interest and motivation than their male peers, as
well as higher levels of anxiety, helplessness and stress in class.
Taken together, the difference between males and females in performance in mathematics, on the one hand,
and attitudes towards the subject, on the other, are highly relevant for policy makers, as these data reveal
inequalities between the sexes in the effectiveness with which schools and societies promote motivation
and interest.
Student performance
The results from the PISA 2003 mathematics assessment revealed an overall gender difference of 11 score
points in favour of males, the largest difference in favour of males being 23 score points in Korea. The only
country with a significant difference in favour of females was Iceland with a 15 score point difference. Some
were are also apparent in the distribution of performance across the six proficiency levels in mathematics.
On the combined mathematics scale, 17% of males on average across the OECD achieved the highest two
proficiency levels (Levels 5 and 6) compared with 12% of females. However, there were no such large
differences at the lowest levels, with 21% of males and 22% of females achieving at Level 1 or below.
In addition to the overall mathematics scale, PISA 2003 provided measures of students’ achievements in
four content areas. Taking account of the research literature on this subject, and following an in-depth
consensus building process among OECD countries on what would be an appropriate basis to compare
mathematics performance internationally, the four content areas established were:
– Space and shape which relates to spatial and geometric phenomena and relationships;
– Change and relationships which involves mathematical manifestations of change as well as functional
relationships and dependency among variables;
– Quantity which involves numeric phenomena as well as quantitative relationships and patterns; and
– Uncertainty which involves probabilistic and statistical phenomena and relationships.
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Overall, the gender differences were largest in the space and shape scale, where there were statistically
significant performance differences between males and females in all OECD countries except Finland,
Norway, the Netherlands and Japan. Across the OECD countries, males performed on average 17 score
points higher than females on this scale. Females outperformed males in only one country, Iceland. The
difference in favour of males reached more than 35 score points, equivalent to half a proficiency level in
mathematics, in the Slovak Republic and in the partner country Liechtenstein (Table 2.1c, OECD, 2004a).
In terms of proficiency levels, gender differences in space and shape were most clearly visible at the top end
of the scale: on average across OECD countries, 7% of males reached Level 6, while only 4% of females
did so. In the Czech Republic, Japan, Korea, the Slovak Republic, Switzerland and the partner country
Liechtenstein, the gender gap at Level 6 was around 6% (Table 2.1b, OECD, 2004a).
On the change and relationships scale males outperformed females in 17 OECD countries and four partner
countries, but generally only by small amounts (Table 2.2c, OECD, 2004a). The average performance
difference between males and females was only 10 score points, a somewhat smaller gap than the 16
score point difference found for the space and shape scale. Only in Iceland did females outperform males.
Nevertheless, as in the case of space and shape, gender differences tended to be larger at the top end of the
scale (Table 2.2b, OECD, 2004a).
Consistent with the findings for the two scales described above, males showed an advantage on the quantity
scale, but gender differences here tended to be even smaller than for either the space and shape or the
change and relationships scales. The distributions of males and females by proficiency level were relatively
similar, with a few more males than females at the top end of the scale (Table 2.3b, OECD 2004a). Sixteen
countries showed differences in favour of males. Again, Iceland was the only country where females
performed statistically significantly above males (Table 2.3c, OECD, 2004a).
Gender differences were also observed in the uncertainty scale in favour of males, where performance
differences occurred in 24 out of the 30 OECD countries.
Trends in gender differences in mathematics between PISA 2003 and PISA 2006
Comparisons of mathematics performance between different cycles of PISA can only be made between
2003 and 2006, since 2003 was the first cycle in which mathematics was the major assessment domain.
Comparisons over such a short time do not necessarily indicate a long term trend and need to be treated
with caution.
Overall there was no change to the gender difference on the combined mathematics scale between 2003
and 2006 - the performance advantage of males remained unchanged at 11 score points. However, there
were changes in gender differences in some individual countries. In Austria, there was a 15 score point
increase in the gender difference in favour of males, the average score for females having decreased by 8
score points while that for males increased by 7 score points. There was a significant decrease in gender
difference in Iceland, the position changing from females having significantly outperformed males in 2003
to no significant difference in 2006: between 2003 and 2006 the average scores for females and males
decreased by 15 score points and 4 score points respectively. In Greece, there was a gender difference
change of 15 score points in favour of females over the period, the average female score having risen by a
significant 21 score points while the male average score rose by 7 score points.
Student attitudes
PISA 2003 showed that gender differences in students’ attitudes and approaches towards mathematics are
even more pronounced than gender differences in performance.
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While performance differences in mathematics tended to be modest, there were marked differences
between males and females in their levels of interest in and enjoyment of mathematics as well as in their
self-related beliefs, emotions and learning strategies related to mathematics. For example, in 21 out of the
40 participating countries and economies males expressed stronger levels of interest in and enjoyment of
mathematics than females. Gender differences in instrumental motivation in mathematics (which is the
motivation related to doing a subject with the aim of gaining an advantage sometime in the future) tended
to be even greater than the gender differences in interest in mathematics, suggesting that males may be more
motivated to learn because they believe that mathematics will help them in their later careers. With respect
to students’ use of learning strategies, gender differences were less pronounced.
A similar picture emerged when looking at students’ mathematics-related self-efficacy beliefs, self-concepts
and anxiety. Females tended to report lower mathematics-related self-efficacy than males in almost
all countries, while males tended to have a more positive view of their abilities than females. Females
experienced significantly more feelings of anxiety, helplessness and stress in mathematics classes than
males in 32 out of the 40 countries and economies.
What did PISA 2003 tell us about gender differences in problem solving?
In PISA 2003, the opportunity was taken by participating countries to implement an innovative assessment
of students’ problem-solving skills. The aim was to design a test that measured students’ cross-disciplinary
problem-solving skills, with performance therefore not dependent on students’ capacities in mathematics
or science. Students were required to identify problems in various settings, choose relevant information or
constraints, represent possible alternatives or solution paths, develop solution strategies, solve the problem
and communicate the solution. The problem-solving component was an integral part of the survey and gave
further information about the link between the analytical reasoning skills needed in mathematics and those
needed in problem solving. The extent to which the advantage of male students in mathematics performance
was replicated in problem solving could give clues as to whether males do better in mathematics because
they have mastered the subject better or because they have particular generic skills that help them solve
mathematical problems.
As in the other assessment areas, scores were standardised to an OECD average of 500 score points and
a standard deviation of 100 – all countries participating in PISA 2003 participated in the problem-solving
assessment. There were three proficiency levels described in problem solving.
Only a few countries showed statistically significant gender differences in problem solving. In Iceland,
Norway and Sweden, as well as in the partner countries Indonesia and Thailand, female students outperformed
male students in problem solving. The partner economy Macao-China was the only participant where
male students outperformed female students. As in mathematics and reading (Table 5.1, OECD, 2004b),
the advantage that female students had in problem solving in Iceland was by far the largest compared to
the other participating countries and economies: female students scored 30 score points more than male
students, representing a third of a proficiency level. In the remaining countries the largest gap in either
direction was 12 score points or less.
In terms of proficiency levels, there were typically slightly more male than female students at both the
lowest and the highest proficiency levels. On average in OECD countries, 18% of male students and
16% of female students were below Level 1, while 19% of male students and 18% of female students
reached Level 3.
The small number of gender differences observed in problem solving may indicate that female and
male students can draw on their own specific strengths when it comes to cross-disciplinary tasks. Male
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students’ strengths in mathematics do not appear to derive from a superiority in analytical reasoning
skills. Rather, it seems that gender-specific strengths balanced out in a way that led to relatively equal
outcomes for both genders in problem-solving performance. This may be an indication that in many
countries there were no strong overall disadvantages for either male students or female students as
learners, but merely gender-specific strengths or preferences for certain subjects.
In this sense, the problem-solving assessment provided a good overall indicator of educational outcomes
for males compared with those of females in individual countries, and hence also an indicator of the
extent to which societies have removed gender-based disadvantages in cognitive performance. Problem
solving is, therefore, an area not affected by particular characteristics of one part of the curriculum that
may favour one group over another.
What did PISA 2006 tell us about gender differences in science?
The remainder of the report focuses on the most recent PISA survey in 2006 when science was the
major domain of assessment. There is a detailed description of the assessment framework and some
sample items are also described. An analysis of results from the computer based assessment of science
(CBAS) is undertaken in an attempt to further understand the reasons behind gender differences in
science.
The results of the most recent PISA survey are described in PISA 2006 – Science Competencies for
Tomorrow’s World (OECD, 2007a). Gender differences on the overall science scale were small. In PISA
2006, for the first time, it was possible to study science performance in detail. As will be described in
more detail in subsequent sections, unlike the position in reading and mathematics, gender patterns in
science were not consistent across subscales. Males outperformed females in explaining phenomena
scientifically while females outperformed males in the scale identifying scientific issues. In the scale
using scientific evidence no clear gender differences emerged. An analysis of gender patterns in different
areas of knowledge revealed some significant differences. Males tended to outperform females in the
areas of “Physical systems” and “Earth and space systems”. The area of “Living systems” however
showed few significant gender differences.
While in many countries the differences between the genders are small relative to differences within
each gender, overall performance could be raised significantly if the factors behind the various gender
differences could be identified and tackled.
The differential gender patterns across the scales highlight the difficulties in designing educational
policies that promote gender equity.
It is also important to note the finding that science assessments must be balanced in their treatment of
the different competencies – for example a test containing an overwhelming percentage of items from
the identifying scientific issues competency would generate the belief that females have an advantage in
science, whereas a test dominated by items from the explaining phenomena scientifically competency
would do the opposite.
The PISA science framework
PISA 2006 gave an opportunity, not available previously, to explore gender differences in each separate
competency and in the knowledge domains. Each of the assessment domains in PISA has a group of experts
which guides the development of the assessment. The PISA 2006 Science Expert Group was charged with
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the responsibility of updating the science framework that had been produced for the first PISA cycle in 2000.
PISA 2006 defined scientific literacy as the extent to which an individual: i) possesses scientific knowledge
and uses that knowledge to identify questions, acquire new knowledge, explain scientific phenomena and
draw evidence-based conclusions about science-related issues; ii) understands the characteristic features of
science as a form of human knowledge and enquiry; iii) shows awareness of how science and technology
shape our material, intellectual and cultural environments; and iv) engages in science-related issues and
with the ideas of science, as a reflective citizen. Scientific literacy requires an understanding of scientific
concepts, as well as the ability to apply a scientific perspective and to think scientifically about evidence.
With more than one-half of the total assessment time devoted to science, PISA 2006 was able to report in
much greater detail on science performance than was the case in PISA 2000 and PISA 2003. As well as
calculating overall performance scores, it was possible to report separately on different science competencies
and to establish for each performance scale conceptually grounded proficiency levels that related student
performance scores to what students are able to do. Students received scores for their capacity in each
of the three science competencies (identifying scientific issues, explaining phenomena scientifically and
using scientific evidence). Estimates were also obtained for students’ knowledge about science (i.e. their
knowledge of the processes of science as a form of enquiry) and knowledge of science (i.e. their capacity in
the science content areas of “Earth and space systems”, “Physical systems”, and “Living systems”).
PISA 2006 also explored student attitudes to various science issues by including questions in the test
instruments, in addition to including questions in the student questionnaire. This was done to put the attitude
questions into a more clearly defined context (an example is given below in Figure 10, Question 10S). In
this way, students’ opinions regarding contemporary science topics were obtained. Two attitude scales were
developed, one on interest in science and the other on support for scientific enquiry.
Student performance
Overall performance in science
Across OECD countries, gender differences in performance on the combined science scale in PISA 2006
tended to be small, both in absolute terms and when compared with the gender gaps in reading and
mathematics performance. For the OECD countries as a whole, there was a statistically significant difference
of two score points between the mean scores of male and female students, in favour of males (Table 2.1a,
OECD, 2007a). Only six OECD countries, the United Kingdom, Luxembourg, Denmark, the Netherlands,
Mexico and Switzerland, showed a statistically significant advantage for males (between 6 and 10 score
points) while two, Turkey and Greece, showed an advantage for females (between 11 and 12 score points).
For the remaining OECD countries there were no statistically significant differences.
As shown in Table 4, in all subject areas and in all countries, males had a greater range of performance than
females. This is demonstrated in the combined science scale by the standard deviation being an average of 6
score points greater for males than for females, with differences of 6 in the competency identifying scientific
issues, 6.3 in explaining phenomena scientifically and 7 in using scientific evidence. The implications of these
results vary from one competency to another according to the mean score comparison. For the competency
identifying scientific issues females outscored males by an average of 17 score points. The greater variation
in results for males suggests that there must be a large number of underperforming males in this competency
and efforts could be concentrated there to seek an improvement in performance. On the other hand for
the competency explaining phenomena scientifically males scored a more highly than females. To gain an
overall improvement efforts should be focused at enhancing the performance of females at the high end of
performance.
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Analysis of the questions which were tested in the field trial for PISA 2006 revealed that some questions
favoured males, some favoured females and others were neutral. In the final selection of items for PISA 2006
it was necessary to have a balance of questions which could provide information about real differences
in the response patterns of males and females. The final selection of items is critical - it is vital that the
composition of the final test reflects a balance of these types of questions to ensure against falsely generating
a gender difference.
Figure 6 shows the percentage of each of the item types in the PISA 2006 science assessment. It can be seen
that this ranges from 5% for closed-constructed responses to 35% for simple multiple choice.
An alternative measure of gender differences in responses can be obtained by examining the percentage of
students who get each item correct. A simple measure of the gender differences for item types is not sufficient
because males and females demonstrated strengths and weaknesses in the three different competencies. An
assessment of the gender difference associated with item types, therefore, needs to keep in mind the average
gender difference for each competency.
It can be seen in Figure 7, for example, that for identifying scientific issues, the average difference in the
percentage correct for all items was 3.3% in favour of females and that for each of the item types there were
differences in the range 3.0% to 3.6%. For explaining phenomena scientifically the overall difference was
2.6% in favour of males, with the largest percentage point difference of 5.5% being for closed-constructed
responses. For using scientific evidence, the overall difference was only 0.2% in favour of females. The
largest difference was 2.7% in favour of males for closed-constructed items. The full list of items, item
format, science competency and the results for males and females is shown in Table 5.
Following are examples of units from the PISA 2006 science assessment. These units, which were publicly
released, demonstrate some gender differences.3 It can be seen that in each example the stimulus is given,
in one case a photo, in another a drawing, and in the third some text. This is followed by the questions.
There is also an indication given of the type of question, the science competency being assessed, the
knowledge being assessed, the application and setting of the question, the difficulty of the question (on a
scale where 500 is the mean score), the proficiency level of the question and the percentage of students
across the OECD who get the question correct. For each question, the correct answer is given in a guide. To
guarantee consistency, the same guide was used by all countries that participated in PISA.
Performance in the competency identifying scientific issues
As noted above, gender differences are visible across the OECD as a whole for two of the three competency
scales. On the identifying scientific issues scale females outperformed males on average across OECD
countries by 17 score points. In a number of countries the advantage of females was large, for example, in
Greece (31 score points) and in the partner countries Qatar (37 score points), Bulgaria (34 score points),
Thailand (33 score points), Jordan (32 score points) and Latvia (31 score points) (Table 2.2c, OECD,
2007a).
It would thus appear that females are better able than males to distinguish scientific issues and content from
other issues. On average, they are better able to recognise questions that can be solved scientifically and
to identify keywords to search for information on a given topic. They can better recognise key features of a
scientific investigation, for example what variables should be used, changed or controlled and what extra
information is needed.
An example of this type of question is contained in the unit GRAND CANYON (Figure 8). This unit has
a brief introductory stimulus and then asks students to identify those questions regarding damage to the
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Figure 6
Percentage of each item type, PISA 2006
Item type
Number of items
Percentage of total
Simple multiple-choice
38
35%
Complex multiple-choice
29
27%
Closed-constructed response
5
5%
Open-constructed response
36
33%
108
100%
TOTAL
Source: PISA 2006 Technical Report (OECD, 2009).
Figure 7
Summary of percentages of correct answers by item types and competencies, PISA 2006
Competencies
Item types
Simple
multiple-choice
Complex
multiple-choice
Identifying scientific
issues
response
Overall difference
Using scientific evidence
Males
Females
Gender
difference
(M-F)
Males
Females
Gender
difference
(M-F)
Males
Females
Gender
difference
(M-F)
54.9
57.8
-3.0
64.2
61.6
2.6
61.9
59.5
2.4
60.8
64.4
-3.6
49.0
47.2
1.8
47.7
47.5
0.2
37.9
32.4
5.5
46.8
44.1
2.7
Closed-constructed
response
Open-constructed
Explaining phenomena
scientifically
25.4
28.4
-3.0
42.8
40.2
2.6
46.0
47.8
-1.8
51.2
54.5
-3.3
52.6
50.0
2.6
50.0
50.2
-0.2
Source: OECD PISA 2006 Database.
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2
Figure 8
Science unit - GRAND CANYON
The Grand Canyon is located in a desert in the USA. It is a very large and deep canyon containing many
layers of rock. Sometime in the past, movements in the Earth’s crust lifted these layers up. The Grand Canyon
is now 1.6 km deep in parts. The Colorado River runs through the bottom of the canyon.
See the picture below of the Grand Canyon taken from its south rim. Several different layers of rock can be
seen in the walls of the canyon.
Limestone A
Shale A
Limestone B
Shale B
Schists and granite
GRAND CANYON – Question 7 (S426Q07)
Level 6
Question type: Complex multiple choice
Competency: Identifying scientific issues
Knowledge category: “Scientific enquiry” (knowledge about science)
Application area: “Environment“
Setting: Social
Difficulty: 485
Percentage of correct answers (OECD countries): 61.3%
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
About five million people visit the Grand Canyon national park every year. There is concern about the
damage that is being caused to the park by so many visitors.
Can the following questions be answered by scientific investigation? Circle “Yes” or “No” for each question.
Can this question be answered by scientific investigation?
Yes or No?
How much erosion is caused by use of the walking tracks?
Yes / No
Is the park area as beautiful as it was 100 years ago?
Yes / No
Scoring
Full Credit: Both correct: Yes, No in that order.
Comment
This is a complex multiple-choice question, where the students must make a selection of “Yes”or “No”for
each of the two options presented. To gain credit a student must correctly answer both of the options
presented, in the order “Yes”, “No”. The student must have some notion of the capacities and limits of
scientific investigations, so the question is assessing the competency of identifying scientific issues. The
setting of the question is located out side the immediate personal life experiences of the student and the
setting is social. The question, at a difficulty level of 485, is just below average difficulty and is placed at
the lower part of Level 3. At this level, students can identify clearly described scientific issues in a range of
contexts.
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environment which can be answered scientifically. Sixty three per cent of females across the OECD answered
this correctly compared to 60% of males.
Performance in the competency explaining phenomena scientifically
In contrast, (Figure 2.16 and Table 2.3c, OECD, 2007a) on the explaining phenomena scientifically scale
males outperform females on average across OECD countries by 15 score points. Again, in some cases this
difference is large – for example among OECD countries it is 25 score points in Luxembourg, 22 in Hungary
and in the Slovak Republic, and 21 in the United Kingdom, Denmark, the Czech Republic and Germany
and in the partner country Chile it is 34 score points The gender differences on this scale are particularly
pronounced at the highest level of proficiency. Across OECD countries the percentage of males in the two
highest proficiency levels (Levels 5 and 6) is 12% compared to 8% for females (Table 2.3b, OECD 2007a).
Students demonstrating this competency can apply appropriate knowledge of science in a given situation.
The competency includes describing or interpreting phenomena and predicting changes, and may involve
recognising or identifying appropriate descriptions, explanations and predictions.
An example of a question assessing this competency is question 3 in the unit PHYSICAL EXERCISE. In this
question, shown in Figure 9, 49% of males scored correctly, compared with 41% of females.
There were exceptions to the general pattern of males performing better than females in explaining
phenomena scientifically. An example is question 4 in the science unit entitled MARY MONTAGU. In this
question, shown in Figure 10, students are asked why vaccination is especially important for the very old
and the very young. Fifty-nine per cent of males scored correctly, compared with 65% of females. This result
might be explained, at least in part, by student interests and career preferences. The question has a focus on
community health, and as is shown in the section on future career planning later in the report, females tend
to have a greater interest than males in health and nursing and intend to follow careers in these areas.
Figure 9
Science unit - PHYSICAL EXERCISE
Regular but moderate physical exercise is good for our health.
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PHYSICAL EXERCISE – Question 1 (S493Q01)
Level 6
Question type: Complex multiple choice
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Personal
Difficulty: 545
Percentage of correct answers (OECD countries): 56.6%
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
What are the advantages of regular physical exercise? Circle “Yes” or “No” for each statement.
Is this an advantage of regular physical exercise?
Yes or No?
Physical exercise helps prevent heart and circulation illnesses.
Yes / No
Physical exercise leads to a healthy diet.
Yes / No
Physical exercise helps to avoid becoming overweight.
Yes / No
Scoring
Full Credit: All three correct: Yes, No, Yes in that order.
Comment
This is a complex multiple-choice question, where the students must make a selection of “Yes”or “No” for
each of the three options presented. To gain credit a student must correctly answer all three of the options
presented, in the order “Yes”, “No”, “Yes”. The student must have some knowledge of the advantages of
physical exercise, so the question is assessing the competency explaining phenomena scientifically. The
question is highly relevant to 15-year-olds as it relates to their own personal health. The question, at a
difficulty level of 545, is of above-average difficulty and is placed at the upper part of Level 3. At this level,
students can select facts and knowledge to explain phenomena and can interpret and use scientific concepts
from different disciplines and can apply them directly.
PHYSICAL EXERCISE – Question 3 (S493Q03)
Question type: Complex multiple choice
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Personal
Difficulty: 386
Percentage of correct answers (OECD countries): 82.4%
What happens when muscles are exercised? Circle “Yes” or “No” for each statement.
Does this happen when muscles are exercised?
Yes or No?
Muscles get an increased flow of blood.
Yes / No
Fats are formed in the muscles.
Yes / No
Scoring
Full Credit: Both correct: Yes, No in that order.
Comment
For this question, to gain credit a student has to correctly recall knowledge about the operation of muscles
and about the formation of fat in the body, i.e. students must have knowledge of the science fact that active
muscles get an increased flow of blood and that fats are not formed when muscles are exercised. This
enables the student to accept the first explanation of this complex multiple-choice question and reject the
second explanation.
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2
The two simple factual explanations contained in the question are not related to each other. Each is
accepted or rejected as an effect of the exercise of muscles and the knowledge has widespread currency.
Consequently, the question is located at Level 1. PHYSICAL EXERCISE, CLOTHES and GRAND CANYON
(Figures 2.29, 2.26 and 2.27) are at Level 1 (below the cut-point), at the very bottom of the scale for the
competency explaining phenomena scientifically.
PHYSICAL EXERCISE – Question 5 (S493Q05)
Question type: Open-constructed response
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Personal
Difficulty: 583
Percentage of correct answers (OECD countries): 45.2 %
Level 6
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
Why do you have to breathe more heavily when you’re doing physical exercise than when your body is
resting?
Scoring
Full Credit:
To remove increased levels of carbon dioxide and to supply more oxygen to your body. [Do not accept “air”
instead of “carbon dioxide” or “oxygen”.] For example:
• When you exercise your body needs more oxygen and produces more carbon dioxide. Breathing does this.
• Breathing faster allows more oxygen into the blood and more carbon dioxide to be removed.
To remove increased levels of carbon dioxide from your body or to supply more oxygen to your body, but not
both. [Do not accept “air” instead of “carbon dioxide” or “oxygen”.]
• Because we must get rid of the carbon dioxide that builds up.
• Because the muscles need oxygen. [The implication is that your body needs more oxygen when you are
exercising (using your muscles).]
• Because physical exercise uses up oxygen.
• You breathe more heavily because you are taking more oxygen into your lungs. [Poorly expressed, but
recognises that you are supplied with more oxygen.]
• Since you are using so much energy your body needs double or triple the amount of air intake. It also needs
to remove the carbon dioxide in your body. [Code 12 for the second sentence – the implication is that
more carbon dioxide than usual has to be removed from your body; the first sentence is not contradictory,
though by itself it would get Code 01.]
Comment
For this question the student must explain how breathing more heavily (meaning deeper and more rapidly)
is related to an increase in physical activity. Credit is given for an explanation that recognises that exercising
muscles requires more oxygen and/or must dispose of more carbon dioxide than when not exercising.
Since the student must recall knowledge in order to formulate an explanation the question belongs in the
knowledge of science category. Relevant knowledge relates to the physiology of the human body, so the
application area is “Health” while the setting is personal.
The student needs to draw on knowledge of body systems in order to relate the gas exchange occurring in
the lungs to increased exercise. Consequently, several pieces of specific knowledge are related in order to
produce an explanation of the phenomenon. This locates the question at Level 4.
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Figure 10
Science unit - MARY MONTAGU
Read the following newspaper article and answer the questions that follow.
the history of Vaccination
Mary Montagu was a beautiful woman. She survived an attack of smallpox
in 1715 but she was left covered with scars. While living in Turkey in 1717,
she observed a method called inoculation that was commonly used there.
This treatment involved scratching a weak type of smallpox virus into the
skin of healthy young people who then became sick, but in most cases only
with a mild form of the disease.
Mary Montagu was so convinced of the safety of these inoculations that she
allowed her son and daughter to be inoculated.
In 1796, Edward Jenner used inoculations of a related disease, cowpox, to
produce antibodies against smallpox. Compared with the inoculation of
smallpox, this treatment had less side effects and the treated person could
not infect others. The treatment became known as vaccination.
MARY MONTAGU – Question 2 (S477Q02)
Level 6
Question type: Multiple choice
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Social
Difficulty: 436
Percentage of correct answers (OECD countries): 74.9%
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
What kinds of diseases can people be vaccinated against?
A.Inherited diseases like haemophilia.
B. Diseases that are caused by viruses, like polio.
C. Diseases from the malfunctioning of the body, like diabetes.
D. Any sort of disease that has no cure.
Scoring
Full Credit: B. Diseases that are caused by viruses, like polio.
Comment
To gain credit the student must recall a specific piece of knowledge that vaccination helps prevent diseases,
the cause for which is external to normal body components. This fact is then applied in the selection of the
correct explanation and the rejection of other explanations. The term “virus” appears in the stimulus text and
provides a hint for students. This lowered the difficulty of the question. Recalling an appropriate, tangible
scientific fact and its application in a relatively simple context locates the question at Level 2.
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MARY MONTAGU – Question 3 (S477Q03)
Level 6
Question type: Multiple choice
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Social
Difficulty: 431
Percentage of correct answers (OECD countries): 75.1%
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
If animals or humans become sick with an infectious bacterial disease and then recover, the type of
bacteria that caused the disease does not usually make them sick again.
What is the reason for this?
A.The body has killed all bacteria that may cause the same kind of disease.
B. The body has made antibodies that kill this type of bacteria before they multiply.
C. The red blood cells kill all bacteria that may cause the same kind of disease.
D. The red blood cells capture and get rid of this type of bacteria from the body.
Scoring
Full Credit: B. The body has made antibodies that kill this type of bacteria before they multiply.
Comment
To correctly answer this question the student must recall that the body produces antibodies that attack
foreign bacteria, the cause of bacterial disease. Its application involves the further knowledge that these
antibodies provide resistance to subsequent infections of the same bacteria. The issue is community control
of disease, so the setting is social.
In selecting the appropriate explanation the student is recalling a tangible scientific fact and applying it in a
relatively simple context. Consequently, the question is located at Level 2.
MARY MONTAGU – Question 4 (S477Q04)
Question type: Open-constructed response
Competency: Explaining phenomena scientifically
Knowledge category: “Living systems” (knowledge of science)
Application area: “Health”
Setting: Social
Difficulty: 507
Percentage of correct answers (OECD countries): 61.7%
Level 6
707.9
633.3
558.7
484.1
409.5
334.9
Level 5
Level 4
Level 3
Level 2
Level 1
Below Level 1
Give one reason why it is recommended that young children and old people, in particular, should be
vaccinated against influenza (flu).
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31
Scoring
Full Credit: Responses referring to young and/or old people having weaker immune systems than other
people, or similar. For example:
These people have less resistance to getting sick.
The young and old can’t fight off disease as easily as others.
They are more likely to catch the flu.
If they get the flu the effects are worse in these people.
Because organisms of young children and older people are weaker.
Old people get sick more easily.
Comment
This question requires the student to identify why young children and old people are more at risk of the
effects of influenza than others in the population. Directly, or by inference, the reason is attributed to young
children and old people having weaker immune systems. The issue is community control of disease, so the
setting is social.
A correct explanation involves applying several pieces of knowledge that are well established in the
community. The question stem also provides a cue to the groups having different resistance to disease. This
locates the question at Level 3.
MARY MONTAGU – Question 10S (S477Q10S)
How much do you agree with the following statements?
Tick only one box in each row.
Strongly agree
Agree
Disagree
Strongly disagree
a) I am in favour of research to develop
vaccines for new strains of influenza.
1
2
3
4
b) The cause of a disease can only be
identified by scientific research.
1
2
3
4
c) The effectiveness of unconventional
treatments for diseases should be subject
to scientific investigation.
1
2
3
4
Performance in knowledge about science
The PISA 2006 framework identified two categories of knowledge about science: i) scientific enquiry which centres on enquiry as the process of science and ii) scientific explanations - which are the results
of scientific enquiry. Across the OECD countries females significantly outperformed males in 15 countries,
with no countries showing an advantage to males. The average difference was 10 score points, with the
largest differences being in Greece, Turkey and Iceland with performance advantages to females of 24, 22
and 20 score points respectively.
Performance in the “Physical systems” area of knowledge of science
An analysis of the knowledge of science content areas by gender revealed some differences. In all OECD
countries except Turkey, males significantly outperformed females in the content area “Physical systems”,
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which relates to the structure and properties of matter, changes of matter and energy transformations. The
OECD country with the largest difference between males and females was Austria with a 45 score point
advantage to males. These Austrian results were mirrored in other comparative studies, most notably the
TIMSS upper secondary assessment (Mullis et al., 1998). Analyses of these data revealed that this gender
gap was closely associated with the difference in the cumulative number of physics lessons which males
and females attended, essentially because of different programmes and study choices (Stadler, 1999). There
were four other OECD countries with an advantage for males 35 score points or more: the Czech Republic,
Luxembourg, Hungary and the Slovak Republic.
In the partner countries and economies the pattern was similar, with males significantly outperforming
females in all but nine countries, the nine exceptions being Qatar, Jordan, Azerbaijan, Bulgaria, Argentina,
Kyrgyzstan, Thailand and Liechtenstein (Table 2.10, OECD, 2007a). Among the partner countries and
economies, the largest differences in favour of males were in Chile (40 score points) and Hong KongChina (34 score points). Other partner countries with differences of 30 or more were Croatia, the Russian
Federation (both 30 score points) and Slovenia (31 score points).
These observations support the popular notion that the physical sciences are the domain of males, a finding
which is mirrored in a much larger share of males among physics graduates (OECD, 2008).
Performance in the “Living systems” area of knowledge of science
In the knowledge of science content area “Living systems”, which refers to cell structure, human biology, the
nature of populations and ecosystems, the gender pattern was less uniform and there were few significant
gender differences. The five OECD countries with significant gender differences in this category in favour of
males were Mexico (13 score points), Hungary (12 score points), and Denmark, Luxembourg and the Slovak
Republic (all 11 score points). The two OECD countries with a significant difference in favour of females
were Greece (12 score points) and Finland (10 score points). Among the partner countries and economies,
there were seven with differences in favour of males and seven in favour of females. The larger differences
in favour of females were in Qatar (37 score points), Jordan (31 score points), Bulgaria (19 score points),
Thailand (13 score points) and Estonia (12 score points). The larger differences in favour of males were in
Chile (27 score points), Chinese Taipei (15 score points), Colombia (13 score points) and Hong Kong-China
(12 score points) (Table 2.9, OECD 2007a).
Performance in the “Earth and space systems” area of knowledge of science
In the content area “Earth and space systems”, which focuses on the structure and energy of the Earth and its
systems, the Earth’s history and its place in space, males tended to outperform females, but there were fewer
significant differences than in “Physical systems”. The largest differences in favour of males in this category
among OECD countries were in the Czech Republic (29 score points), Luxembourg (27 score points), Japan,
Switzerland and Denmark (all 26 score points) and the Netherlands (25 score points) and in the partner
countries Chile (35 score points), Colombia (26 score points), Israel and Uruguay (25 score points) (Table 2.8,
OECD 2007a).
Gender differences within schools
A further issue which is relevant to the gender differences observed in PISA is that males and females,
in some countries, make different choices in terms of the schools, academic tracks and/or educational
programmes they attend. PISA 2006 compared the observed gender difference in science for all students
within countries, then considered the gender differences within schools both before and after taking into
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33
account the various programme and school characteristics that might influence performance (further analysis
of this issue is undertaken in the section on socio-economic background later in the report).
In most countries, gender differences in science performance were much larger within schools than they
were in the country overall (Table 2.5, OECD, 2007a). While across the OECD the average overall gender
difference in PISA 2006 was 2 score points in favour of males, the within-school gender difference was an
average of 8 score points in favour of males. This difference increased to 9 score points after accounting
for the programme level and destination in which students are enrolled. In most countries the higher
within-school gender difference may reflect the fact that females tend to choose the higher performing,
academically oriented tracks and schools at a higher rate than males.
A typology of gender differences in science
Gender differences in science performance paint a complex picture but a general pattern is discernible.
There appear to be three different types of countries with respect to gender differences across countries and
within schools.
In a first group of countries, type A, gender differences are small and insignificant overall, for each of the three
competency scales of science performance and also within schools. A good example of a type A country
is Australia where there is no overall gender difference observed for science and there is an insignificant
difference within schools, both before and after accounting for programme level and destination. Finland,
Iceland, Ireland, Japan, Korea, New Zealand, Norway and Sweden are OECD countries similar in this
respect. Among the partner countries, Estonia also falls into this category.
In the second group of countries, type B, there is an insignificant overall gender difference but gender
differences within school are significant, even after accounting for programme level and destination.
Hungary is a good example of a type B country within the OECD. It shows a statistically insignificant
difference of 6 points in favour of males in the mean science score. The gender difference within schools in
Hungary, however, is large and significant, 27 score points in favour of males. Other type B countries with
a large disparity within schools (more than 10 score points) were France, Belgium, the Czech Republic, the
Slovak Republic, Germany and Italy and the partner countries and economies Serbia, Croatia, Romania,
Tunisia, Hong Kong-China, Macao-China, Montenegro and Uruguay (Table 2.5, OECD, 2007a).
In the third group of countries, type C, gender differences are significant for both the overall science score
and within schools (before and after accounting for programme level and destination). The United Kingdom,
Luxembourg and Denmark are examples of type C countries with consistently high gender differences in
favour of males.
What are the main drivers of gender differences across and within countries? Why is one country type A, B
or C? The structure of the education system and specific educational policies play a role but there may also
be pressures operating outside the school which may contribute to gender differences. For example, in PISA
2003, the performance advantage of females in all subject areas in Iceland, most notably in rural areas has
for example, been attributed to labour-market incentives that deter males in rural areas from focusing on
academic studies which are seen by females as a lever to social and regional mobility (Ólafsson et al., 2003).
Computer-based assessment of science (CBAS) in PISA 2006
In PISA 2006, countries were given the option of participating in a computer-based assessment of science.
The aim of this was to assess some facets of the science framework which were not easily assessed by
a pencil-and-paper test. The tasks given to students were often video based, involving a simulation or
otherwise displaying a problem that could not be done so easily or effectively on a pencil-and-paper test.
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The tests were delivered to a sub-sample of the students at a school undertaking the PISA assessment, using
a set of standardised computers. CBAS was implemented in 2006 in Denmark, Iceland and Korea. Their
mean scores in the computer-based assessment of science were 463, 472 and 504 points, respectively. This
compares to the same students’ mean scores in the standard PISA science assessment of 481, 471 and 502
points, respectively (note, however, that these CBAS scores are not directly comparable to the normal PISA
mean scores as they were analysed separately).
One of the goals of the computer-based assessment of science was to reduce the reading load of the
questions, at the same time retaining the science content. It was found that the correlation between scores
on the computer-based assessment of science and scores in PISA reading, at 0.73, was lower than the
correlation between PISA science and PISA reading scores (0.83), so by this measure the goal of reducing
the reading load was successful.
In Figure 11 it can be seen for each of the three countries there was a significant gender difference in favour
of males in the computer-based assessment of science: 45 score points in Denmark, 25 score points in
Iceland and 26 score points in Korea. It can also be seen (Table 6) that the gender differences in scores on
the paper-and-pencil science test for the same students were much less: 23 score points in favour of males,
a statistically insignificant 7 score points in favour of females and a statistically insignificant 1 score point in
favour of females in Denmark, Iceland and Korea respectively. The decreased reading load in the CBAS may
be the reason why males score higher in that form of assessment.
PISA will continue with the development of computer-delivered testing in PISA 2009 – this time with a focus
on assessing students’ skills in searching for, reading, and understanding material presented on computers.
One might also conclude, given the significant differences in these results and the relative lack of interest
by females in pursuing tertiary education in computer science, that some countries may benefit from a
programme in school to make this area more appealing. Given that there could be a need to increase the
Figure 11
Differences in results in the computer-based assessment of science (CBAS) by gender, PISA 2006
Student performance
540
� Males
� Females
520
500
480
460
440
420
400
Korea
Source: OECD PISA 2006 Database, Table 6.
Iceland
Denmark
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35
total number of computer science graduates, and that the number of male students in this field may be near
its maximum, this is especially important.
Student attitudes
Unlike the position in mathematics and reading, gender differences in science performance cannot be
traced back to gender differences in attitudes, motivation or confidence. In PISA 2006, countries focused
attention on student attitudes to science both inside and outside the classroom. Male and female students
report similar attitudes, motivations and confidence regarding science. PISA 2006 sought information
on attitudinal measures comprising self-efficacy, self-concept, interest in science, enjoyment of science,
instrumental motivation to learn science, career intentions, awareness of environmental issues, optimism
regarding environmental issues, and responsibility for sustainable development and looked at the gender
differences in attitudes using effect sizes. An effect size allows a comparison of differences between males
and females across measures that differ in their metric as, for example, between the PISA indices and the
PISA test scores (Table 3.21, OECD, 2007a).
To assess self-efficacy in PISA 2006, students were asked to rate the ease with which they believed they
could perform eight listed scientific tasks. On average in OECD countries, each unit increase on the index
of self-efficacy in science corresponded to a performance difference of 38 score points (Table 3.3, OECD,
2007a), with the majority of countries showing no gender differences on the index. In the PISA 2003
mathematics assessment, males reported higher levels of self-efficacy in mathematics both overall and in
many countries, whereas in PISA 2006 males reported higher levels of self-efficacy in science only in Japan,
the Netherlands, Iceland and Korea and in the partner economy Chinese Taipei.
Students’ academic self-concept is both an important outcome of education and a trait that correlates strongly
with student success. It can also affect other factors such as well-being and personality development, factors
that are especially important for students from less advantaged backgrounds. In contrast to self-efficacy in
science, which asked students about their level of confidence in tackling specific scientific tasks, the index
of self-concept measured the general level of belief that students had in their academic abilities. PISA 2006
showed gender differences in students’ self-concept in science, but they tended to be small to moderate
(Table 3.21, OECD, 2007a). In 22 OECD countries and 8 partner countries and economies, males were
more likely than females to agree that learning school science topics was easy or that they could give good
answers to test questions on science topics. On average, gender differences in self-concept in science were
slightly less than those in mathematics in PISA 2003.
Levels on the index of general interest in learning science were similar for males and females across most
participating countries while in the majority of countries there were no gender differences on the index
of enjoyment of science (Table 3.21, OECD, 2007a). Males and females also reported similar levels of
instrumental motivation to learn science in the majority of countries.
Similar proportions of male and female 15-year-olds reported that they would like to work in a career
involving science, continue to study science after secondary school, work on science projects as adults or
spend their life doing advanced science. However, there were small gender differences in some countries
on the index of future-oriented motivation to learn science, with more males than females, on average, being
motivated to learn science because they wanted to use it in the future. This was the case in Japan, Greece,
Korea, Iceland, the Netherlands, Italy and Germany, as well as in the partner countries and economies
Hong Kong-China, Qatar, Macao-China and Chinese Taipei where the gender difference in favour of males
was quite large. The Czech Republic was the only participating country where females reported higher
levels of future-oriented motivation to learn science (Table 3.21, OECD, 2007a).
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On average, males and females reported similar attitudes toward the environment, although there were some
gender differences among participating countries (Table 3.21, OECD, 2007a). In general, males were more
aware than females about environmental issues, with significant differences in 12 OECD countries, although
females were more environmentally aware in the partner countries Jordan, Thailand and Kyrgyzstan. The
index of awareness of environmental issues had the strongest relationship with science performance among
the attitudinal measures in PISA 2006 and was associated with better performance in all participating
countries.
Regarding the outlook for selected environmental issues over the next 20 years, the position varied quite
significantly from country to country. Males were more optimistic than females in 12 OECD countries and
in three partner countries and economies, though the gender differences tended to be small. In contrast,
females reported stronger levels of concern for environmental issues in 16 OECD countries and in 8 partner
countries and economies. Higher values on the index of optimism regarding environmental issues were
linked with lower science performance. Males in Finland, Norway, the United Kingdom and Germany were
both more aware of and more optimistic about environmental issues (Table 3.21, OECD, 2007a).
Similarly, there were small gender differences on the index of students’ responsibility for sustainable
development in nine countries: Finland, Iceland, Denmark, Norway, Sweden, Canada, Australia,
New Zealand and the partner country Thailand. In all these countries females reported higher levels of
responsibility (Table 3.21, OECD 2007a).
Science performance and attitudes towards science
PISA 2006 found that there is no significant relationship, at the country level, between the average level
of gender differences in attitudes and the overall average student performance in a country, that is a large
gender difference is equally likely to be apparent with a high performing or a low performing country.
For example, Finland, with a quite high total of six (out of nine) significant gender differences in attitudes
has the highest score of 563 score points whereas the partner country, Estonia, with no significant gender
differences in attitudes also scores very highly with 531 score points. At the other end of the scale, some
countries with no significant gender differences in attitudes have scores well below the OECD average – for
example, Azerbaijan (382 score points), Tunisia (386 score points), Montenegro (412 score points), Romania
(418 score points) and Serbia (436 score points). Among the countries with a relatively high number of
gender differences in attitudes scoring below the OECD average are Thailand (421 score points), Greece
(473 score points) and Norway (487 score points). It appears then that there is no clear relationship between
gender differences in attitudes and overall performance in science, at the country level.
Within countries, however, there are several different patterns of relationships between gender differences
in attitudes and gender differences in performance. A number of participating countries showed no gender
differences in either science performance or attitudes towards science (Table 3.21, OECD, 2007a). These
included Portugal and the partner countries Azerbaijan, Israel and Montenegro. In another group of
countries (Ireland, Mexico, Poland, the Slovak Republic, Spain, and the partner countries Argentina, Brazil,
Colombia, Croatia, Estonia, Indonesia, Romania, the Russian Federation, Serbia, Tunisia and Uruguay),
there were moderate gender differences in a maximum of two of the measures, whether performance or
attitudinal.
Another group of countries, was characterised by a combination of similar performance by males and
females, but with significant differences in the attitudes of male and female 15-year-olds. Gender differences
in attitudes were most prominent in Germany, Iceland, Japan, Korea, the Netherlands and the United
Kingdom, and in the partner economies Chinese Taipei, Hong Kong-China and Macao-China where males
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37
recorded higher values on at least five of the attitudinal measures (although in Iceland, Germany and the
Netherlands females reported either higher concern for environmental issues or higher responsibility for
sustainable development). To a lesser extent this was the case also in France, Italy, and the United States.
In Austria, Greece, Iceland, Korea and Norway, females had more negative attitudes on at least three of
the attitudinal measures, despite the fact that they perform better on the identifying scientific issues scale.
Conversely, in the partner countries Jordan and Thailand, females both performed better on the science
assessment and reported more positive science attitudes (Table 3.21, OECD, 2007a).
Student background
Socio-economic background
What is the relationship between gender and student socio-economic background? Do 15-year-old male
students tend to have more advantaged socio-economic background than female students, or vice versa?
The measurement of equity of educational outcomes has been a focus of PISA analyses since the first survey
in 2000. In each of the initial reports a chapter has been dedicated to this issue. To construct a measure of
socio-economic background students were asked a number of questions in the student questionnaire about
their parents’ occupation and education, and about some features that may be present in their homes. The
PISA index of economic, social and cultural status (ESCS) was created to capture wide aspects of a student’s
family and home background.4
In most countries, there is no difference between the average scores of males and females on the PISA index
of economic, social and cultural status. However, there are significant differences between them in some
countries (see Table 7). On average, females in PISA 2006 were socio-economically more advantaged than
males only in the partner economy Hong Kong-China, while males were socio-economically more advantaged
than females in Luxembourg, Italy, Poland, Iceland, Sweden, and Norway, as well as the partner countries
Israel, Estonia, Montenegro, Brazil and Latvia. This gender difference in students’ socio-economic background
could be due to males with disadvantaged socio-economic background being less likely than females with
equally disadvantaged socio-economic backgrounds to stay at school until the age of 15.
Since students’ socio-economic background is associated with performance (OECD, 2007a), the observed
gender differences in countries’ average science performance could be affected by the imbalance in the average
socio-economic background between males and females. Among countries with a gender difference in average
socio-economic background a change in the gender difference in performance in science before and after
adjusting for students’ socio-economic background can be observed only in Iceland and the partner country
Estonia (Table 8). In these two countries, there was no gender difference in the raw performance data, but after
adjusting it to account for socio-economic background, females performed slightly higher than males.
The PISA 2006 initial report (OECD, 2007a) shows that across the OECD countries there is a 40 score point
difference in science performance associated with a one standard deviation difference in socio-economic
background. This measure of the effect of socio-economic background on performance is known as the socioeconomic gradient (Willms, 2006). While the socio-economic gradients vary greatly among OECD countries,
from 25 score points per standard deviation in Mexico to 54 score points in France, the socio-economic
gradients do not vary between males and females in any OECD country except the Czech Republic and
Austria. In each of these countries the gradient is higher for females than for males. In the Czech Republic
the gradient for males is 43 score points, whereas for females it is 60 score points; in Austria the difference
is slightly smaller with 40 score points for males and 52 for females. This means that, on average, in both the
Czech Republic and Austria, the score for a female student will increase more than it will for a male with the
same rise in socio-economic background.
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
It has been shown that the relationship between students’ socio-economic background and performance does
not differ between males and females (except for Iceland and Estonia as described above). But what is the
effect of the average socio-economic background of a school’s student body, that is of the school contextual
effect?
As described in an earlier section, the PISA 2006 initial report (OECD, 2007a) showed that gender differences
in performance within schools were much larger than gender differences in performance in the countries
overall. Do within-school gender differences in performance vary according to the school’s socio-economic
intake? Or is the within-school gender difference consistent regardless of school average socio-economic
background?
A two-level regression analysis by country was conducted.4 In 23 OECD countries, the within-school gender
difference in science is consistent across schools with varying levels of socio-economic intake (Table 9). In
some countries, however, the within-school gender difference varies according to the level of schools’ socioeconomic intake. The countries for which this is the case include Australia, Austria, Belgium, Spain, France,
Italy and the Slovak Republic. As Figure 12 shows, in Australia there is no gender difference in performance
in schools which have the national average socio-economic intake, but in schools with a more advantaged
socio-economic intake (i.e. schools with average socio-economic background one standard deviation above
the national average ESCS) males outperform females, while in schools with a more disadvantaged socioeconomic intake average females outperform males. Another example is Austria, where males outperform
females in general but this within-school gender gap decreases as the school socio-economic intake increases.
The opposite pattern is observed in Belgium, where the within-school gender difference favouring males
increases as the school socio-economic intake increases.
These different patterns in within-school gender difference according to the level of school socio-economic
background are important from a policy perspective – and for school principals and teachers – as strategies
and interventions for mitigating any within-school gender gap in performance need to be adapted according
to the level of school socio-economic intake in those countries in which within-school gender differences vary
across schools according to their socio-economic intake.
Immigrant status
In most OECD countries, increasing attention is being paid to issues surrounding migration. In part, this is
a consequence of the growth of migration flows - between 1990 and 2000, the number of people living
outside their country of birth nearly doubled worldwide, to 175 million (OECD, 2006c). Among 15-year-old
students, the proportion of students who were foreign born or who had foreign born parents exceeded 10% in
many PISA countries, being higher than 35% in Luxembourg, Liechtenstein, and in the partner countries and
economies Macao-China, Hong Kong-China and Qatar (Table 4.2c, OECD, 2007).
Native students are defined as those who were born in the country of assessment and who have at least
one parent born in the country of assessment. First-generation students are those students who came to the
country of assessment as a migrant and whose parents were also born outside that country. Second-generation
students are those who were born in the country of assessment, but whose parents were born outside the
country of assessment. Among the countries with significant numbers of 15-year-olds with an immigrant
background,6 first-generation students lag, on average, 58 score points behind their native counterparts. Much
of this difference remains even after accounting for socio-economic factors. Comparing native females with
immigrant females and native males with immigrant males echoes this result (Table 10).
When examining the difference between native students and immigrant students, the extent to which there
is a difference between the way that males and females settle into a new country (as indicated by school
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39
Figure 12
Within-school gender difference in science performance, by school ESCS
■
In schools with one standard deviation below the national average of school ESCS
■
In schools with the national average of school ESCS
■
In schools with one standard deviation above the national average of school ESCS
Score point difference
60
40
Females outperform males
20
0
20
Males outperform females
Thailand
Slovenia
Romania
Macao-China
Jordan
Israel
Croatia
Slovak Republic
Italy
France
Spain
Belgium
Austria
Australia
40
Source: OECD Pisa 2006 Database, Table 9.
Figure 13
Percentage of students whose parents report various science activities at age 10
� Males
%
30
� Females
25
20
15
10
5
0
Reading science
fiction
Watching science
TV programmes
Reading science
books
Visting science
websites
Belonging to a
science club
Source: OECD Pisa 2006 Database, Table 11.
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performance) could provide policy makers with valuable information in planning induction programmes for
migrant students. This can be done using PISA data. Put simply the measure is a comparison of the difference
for immigrant and native females and the difference for immigrant and native males. A larger measure will
indicate more difficulty settling into the new country. In only one country is there a statistically significant
difference between males and females – that is Qatar where there is a 16 score point advantage to males. In all
the other countries there was no statistically significant difference between males and females (see Table 10).
Parental involvement
Can data provided by parents shed any light on gender differences in science performance? In PISA 2006,
16 countries implemented a questionnaire for parents of students participating in PISA. The data collected
from these questionnaires provided information about the activities that children undertook when they were
younger. The data in PISA 2006 showed that parents reported that both males and females had equal access
to different types of science activities at age 10.
It was found that there was a strong association between a student’s involvement in science-related activities
around age 10 and performance in PISA at age 15. Students whose parents reported that their child had, at
age 10, read books on science “very often” or “regularly” performed an average of 39 points higher than did
students whose parents reported that their children had done this “never” or “only sometimes”. As shown
in figure 13 there were no gender differences in the extent to which males and females undertook these
activities as 10 year olds.
To obtain information on parents’ impressions of the quality of the schools that their sons and daughters
attend, they were asked their views on the following statements: i) Most of my child’s school teachers
seem competent and dedicated; ii) Standards of achievement are high in my child’s school; iii) I am happy
with the content taught and the instructional methods used in my child’s school; iv) I am satisfied with the
disciplinary atmosphere in my child’s school; v) My child’s progress is carefully monitored by the school;
vi) My child’s school provides regular and useful information on my child’s progress; and vii) My child’s
school does a good job in educating students. Parents responded using a four point scale: “strongly agree”,
“agree”, “disagree” and “strongly disagree”.
Figure 14 shows the extent to which parents agreed with three of these statements: that the teachers are
competent, that they are satisfied with the disciplinary atmosphere and that the standards of achievement
are high. It can be seen that there are no significant differences between the perceptions of the parents of
male students and the parents of female students.
Future career orientation
It is possible future career opportunities may provide an incentive for students to perform better. Students’
career preferences and the extent to which they are well informed about science-related careers, are
measured by PISA 2006. The data suggested that students who demonstrated strong scientific skills and the
required competencies to pursue more advanced scientific studies did not tend to report aspiring to science
careers unless they also valued or enjoyed science.
Information on science-related careers and preparation for the future
Earlier in this report it was seen that females entering tertiary level science courses tend to opt for the
life sciences and avoid the computer sciences (Figure 3). In PISA 2006 students were asked about their
perceptions of how well their school has prepared them for the future in terms of science-related careers.
The index of school preparation for science-related careers was derived from students’ level of agreement
with the following statements: i) the subjects available at my school provide students with the basic skills
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41
Figure 14
Percentage of students whose parents agree with statements regarding
the school of their son or daughter
�
%
100
Males
�
Females
"Most of my child’s school teachers seem competent and dedicated"
80
60
40
20
%
100
Turkey
Qatar
Portugal
Poland
New Zealand
Macao-China
Luxembourg
Korea
Italy
Iceland
Hong Kong-China
Denmark
Croatia
Colombia
Bulgaria
0
"I am satisfied with the disciplinary atmosphere in my child’s school"
80
60
40
20
Luxembourg
Macao-China
New Zealand
Poland
Portugal
Qatar
Turkey
Macao-China
New Zealand
Poland
Portugal
Qatar
Turkey
Korea
Italy
Iceland
Hong Kong-China
Denmark
Croatia
Luxembourg
%
100
Colombia
Bulgaria
0
"Standards of achievement are high in my child’s school"
80
60
40
20
Korea
Italy
Iceland
Hong Kong-China
Denmark
Croatia
Colombia
Bulgaria
0
Source: OECD PISA 2006 Database, Table 12.
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and knowledge for a science-related career; ii) the science subjects at my school provide students with the
basic skills and knowledge for many different careers; iii) the subjects I study provide me with the basic
skills and knowledge for a science-related career; and iv) my teachers equip me with the basic skills and
knowledge I need for a science-related career. A four-point scale with the response categories “strongly
agree”, “agree”, “disagree” and “strongly disagree” was used. As with other indices constructed for PISA
2006, this index had an average across OECD countries of zero and a standard deviation of one. Students
with a score below zero do not necessarily have a negative view of the question, it is just that they have a
less positive view than the OECD average.
Across the OECD there was no overall difference between males and females on this measure. The OECD
country with the largest difference in favour of males on this index was Greece (a difference of 0.18), while
the largest difference among the partner countries and economies was in Macao-China (0.23).
The students were also asked for their perception of the quality of advice available to them at school
about science-related careers. The index of student information on science-related careers was derived from
students’ beliefs about their level of information on the following topics: i) science-related careers that are
available in the job market; ii) where to find information about science-related careers; iii) the steps students
need to take if they want a science-related career; and iv) employers or companies that hire people to work
in science-related careers. A four-point scale with the response categories “very well informed”, “fairly
informed”, “not well informed” and “not informed at all” was used.
Across the OECD there was a small but significant gender difference observed in the index of student
information on science-related careers in favour of males. There were 13 OECD countries and 15 partner
countries and economies where males perceived themselves to be better informed than females in this
regard. Conversely there were five OECD countries and one partner country where females perceived
themselves to be better informed than males. The largest difference in an OECD country was in Mexico (a
difference of 0.20 on the index in favour of females), and among the partner countries, Qatar had the largest
difference (0.25 in favour of females).
Do students expect to pursue a scientific career?
There were marked differences in the preferred science careers of male and female students who participated
in PISA.
In PISA 2006 students also reported their expected career at age 30. From these responses it was possible to
identify those students who expected to pursue a science-related career. This was done by coding students’
responses using the international standard classification of occupations (ISCO-88 - see Annex A10, OECD
2007a ). In accordance with this definition, science-related careers include those that involve a considerable
amount of science, plus careers that involve tertiary education in a scientific field. Thus it includes careers
that are beyond the traditional idea of a scientist, such as engineer, weather forecaster, optician and medical
doctor.
The percentage of students expecting a science-related career is an indicator of an important educational
outcome. In countries where policy makers are concerned about shortages of science professionals in the
labour market, analysis of students reporting that they expected science-related careers, in conjunction
with other background factors such as the socio-economic background of students and schools, study
programmes and gender, could help to identify in which student groups, and to what extent, science
orientation may be less pronounced. On average across OECD countries, 25% of students reported that
they expected to be in a science-related career at age 30 (Table 3.12, OECD, 2007a). Between 35 and 40%
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
43
of students reported that they expected a science-related career in Portugal, the United States and Canada,
and in the partner countries Chile, Jordan and Brazil.
In broad terms, PISA 2006 showed only small differences in the kinds of jobs males and females expected to
have when they are 30 years old: on average, 27% of females reported that they expected to have a sciencerelated career at age 30, compared to 24% of males (Table 3.12, OECD, 2007a). However, when looking at
the particular type of science job that students indicated, there were some large differences between males
and females.
Across the OECD 17% of males who expected a scientific career chose computer sciences compared to
2% of females, with no country showing a higher percentage for females (Table 15). In some countries the
difference was very large. In the Slovak Republic, for example, 44% of males who expected a scientific
career chose computer sciences compared to 2% of females.
There were similar figures for technicians where, across the OECD, an average of 16% of males who
anticipated a scientific career expected to be a technician compared with 5% of females. There were
no OECD countries where a higher percentage of females than males expressed an expectation to be a
technician. The largest gender differences were in Poland, with a difference of 30% between males and
females (33% for males and 2% for females), and Austria with a difference of 31% (38% for males and 6%
for females).
On the other hand there were also occupations which females preferred much more than males. One
of these was nursing: across the OECD, 30% of females who expressed an interest in a scientific career
expected to be involved in nursing compared with 4% of males. In Belgium the equivalent figures were
44% of females compared with 7% of males. Another such career area was other occupations relating to
health (including medical doctor, dentists, veterinarians and pharmacists) where, across the OECD, 42%
of females who expressed an interest in a scientific career expected to be involved compared with 20% of
males. In France the equivalent figures were 58% of females and 18% of males.
In PISA 2006, an index was constructed of student responses to the questions: i) I would like to work in a
career involving science; ii) I would like to study science after secondary school; iii) I would like to work
on science projects as an adult; and iv) I would like to spend my life doing advanced science. This index is
known as the index of future-oriented motivation to learn science and it was found in PISA 2006, that across
the OECD a change of one unit in the index was associated with 19.7 score point increase on the science
literacy scale.
These observations of 15-year-olds in 2006 are very similar to the patterns of uptake of tertiary education
and careers which were described earlier in this report for people (students and employees) who were
aged in their twenties. This suggests that there is a very long time-lag for changes to occur. This means that
governments which wish to implement changes should do so without delay and should expect this to be a
long term project.
School organisation
Single sex-schooling
Much research has been done in the area of mixed- and single-sex schooling. The generally accepted view
has been that for females, single-sex schooling is more advantageous, whereas for males, mixed-sex schooling
is more favourable. Riordan (1994) wrote that females do better academically in single-sex schools and
colleges, across a variety of cultures, and that on the basis of the available research, it appeared that
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
“single-sex schools for females provide a greater opportunity for educational attainment as measured by
standardised cognitive tests, curriculum and course placement, leadership behaviour, number of years of
formal education, and occupational achievement.”
There is not unanimous agreement on this issue. In the United Kingdom, one study (Malacova, 2007)
carried out multilevel modelling on national value-added data to study the effects of single-sex education
on the progress of pupils from 2002 Key Stage 3 to 2004 General Certificate of Secondary Education (GCSE).
The analysis suggested that pupils in a selective environment achieve higher progress in single-sex schools;
however, the advantage of single-sex schooling seems to decrease with increasing prior attainment (for
females) or with increasing school ‘selectiveness’ (for males).
PISA can help to throw some light on this issue.7 In order to have a sufficient number of cases to analyse
in PISA, there must be a minimum of 3% of the sample or 30 cases.8 In the analysis of single-sex schools,
this excludes many countries as they have either no or very small numbers of students attending this type
of school.
For the countries that did qualify for analysis there are some interesting findings. Analysis was carried out to
find the differences in science performance for students (males and females separately) attending single-sex
schools and mixed-sex schools. It was also decided to undertake the analysis taking into account both the
students’ and the schools’ socio-economic background, given that in some countries single-sex education
is associated with higher socio-economic intake, fee-paying schools.
There are, therefore, three sets of results (Table 16) – one set for the raw difference in scores between singlesex and mixed-sex schools for males and females, another for the differences after accounting for students’
socio-economic background, and a third after accounting for both students’ and schools’ average socioeconomic background.
It can be seen in Figure 14 and 15 that, generally, the differences tend to diminish after both students’ and
schools’ socio-economic background are taken into account for both males and females. For males there
was a significant difference in Korea, Australia and the partner country Thailand between single-sex schools
and mixed-sex schools after accounting for students’ socio-economic background, but this disappeared
after taking schools’ socio-economic background into account as well. Significant differences remained
in the partner countries and economies Chinese Taipei and Chile where the difference favoured single-sex
schools and Macao-China, Jordan and Qatar where the difference favoured the mixed-sex schools.
For females there was a significant difference in Ireland, Luxembourg, Australia and the partner country
Thailand between single-sex schools and mixed-sex schools after accounting for student’s socio-economic
background, but this disappeared after taking school’s socio-economic background into account as well.
After taking both student’s and school’s socio-economic background into account there were significant
differences in the United Kingdom, New Zealand and the partner economy Macao-China, where the
difference favoured single-sex schools and in Japan, Turkey and the partner countries Chile and Qatar where
the difference favoured the mixed-sex schools.
Generally speaking, in terms of science performance, the evidence from PISA does not uniformly support
the notion that females tend to do better in a single-sex environment.
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45
Figure 15
Difference in performance of females between single-sex and mixed sex schools, PISA 2006
■
Raw difference in score (single - mixed)
%
125
●
■
Difference after accounting for students ESCS
Difference after accounting for students and schools ESCS
100
75
50
25
0
-25
-50
-75
-100
Thailand
Chinese Taipei
New Zealand
United Kingdom
Source: OECD PISA 2006 Database, Table 16.
Hong Kong-China
Macao-China
Colombia
Australia
Ireland
Israel
Korea
Japan
Jordan
Qatar
Chile
Luxembourg
Turkey
-125
Figure 16
Difference in performance of males between single-sex and mixed sex schools, PISA 2006
�
Raw difference in score (single - mixed)
%
125
�
�
Difference after accounting for students ESCS
Difference after accounting for students and schools ESCS
100
75
50
25
0
-25
-50
-75
-100
Chinese Taipei
Chile
Thailand
Australia
Korea
Hong Kong-China
Ireland
Colombia
New Zealand
Japan
Israel
Qatar
Macao-China
United Kingdom
Jordan
-125
Source: OECD PISA 2006 Database, Table 16.
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Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Homework
Homework is used for a number of purposes in schools, but is most usually aimed at allowing students to
practise concepts learned during the day to reinforce them, and to encourage students to learn research
skills using libraries, the Internet or other resources such as books at home.
In PISA, to find out if students pursued their separate subject areas in studies outside school, they were asked
to indicate in addition to their classroom hours, for each of science, reading and mathematics:
• the time spent attending out-of-school-time lessons (at school, at home or somewhere else) – referred to
as “out of-school lessons” below;
• the time spent studying or doing homework by themselves – referred to as “self-study” below.
For both these components, the time spent on work outside school was categorised into two or less hours
per week and more than two hours per week. It can be seen (Table 17) that most students spend two hours
or less doing work outside school and that on average across the OECD there are few differences between
males and females for all three subject areas.
The data on time spent on self-study shows greater variation between males and females. For science, on
average across the OECD, a greater percentage of females (27%) than males (23%) spent two or more hours
per week on self-study. The outcomes were similar in both mathematics (38% of females spent two or more
hours per week on self-study compared to 32% of males) and reading (36% of females compared to 26% of
males). So in each of the subject areas females spend more time on self-study than males.
In science, for example, there was a large difference between males and females in Poland with 31% of
males spending two or more hours per week on homework compared to 52% of females. For mathematics
the largest difference occurs in Poland with 35% of males reporting two or more hours per week compared
to 50% of females and for reading the figures are even a little greater, 36% for males and 57% for females.
In Italy, 73% of females report that they spend two or more hours on reading homework per week – this is
the highest percentage of students in any of the three subject areas.
The observation that females spend more time on homework is in accord with other research in the area.
Wagner, Schober, and Spiel (2008) concluded this in a paper that presents three studies which deal with the
time 15-year-old students spend working at home for school. Using diaries as a data collection method they
found that the students invested on average 11.7 hours per week in work done at home for school and that
females spent more time than males.
Rogers and Hallam (2006) in a study of GCSE students in the United Kingdom also found the same gender
differences. Interestingly, their findings suggested that, overall, high-achieving males have better studying
strategies than high-achieving females and that they achieve high standards while doing less homework.
Conclusion
The extent to which males and females have different outcomes in education and the labour market is an
extremely complex discussion. This report shows that there are, indeed, significant differences in many
areas. The evolution of these differences provides some challenging issues for parents and educators.
At the primary education level, studies by the IEA indicate few gender differences in science and mathematics,
but a clear advantage to females in reading.
At the secondary level, international data confirm an on-going advantage for females in reading, but also
show differences in performance in favour of males in some areas of both mathematics and science. In
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
47
fact, there has been an increase in the gender differences observed in reading from 2000 to 2006. It would
seem that policy makers and educators could take note of this trend and investigate ways to arrest its
development. The data also show that male and female students are making different tertiary education
and career choices in relation to science, with females showing a preference for health related courses
and males for computing science. Taking all the observations and analyses together it seems that gender
differences are actually increasing as students get older.
Female students have much higher levels of interest in reading than males, with the converse being true in
relation to mathematics. The results of PISA 2003 mathematics showed that females generally had higher
levels of anxiety about mathematics and that the level of anxiety is associated with performance. At the
same time, there were no major gender differences in problem solving – leading to the conclusion that
females’ capacity in mathematics could be expanded if the levels of anxiety were lower. In relation to
science, however, interest and engagement do not differ significantly between male and female students.
In summary, the results show that schools and societies do not always succeed in fostering comparable
levels of motivation, interest or self-confidence in different areas among male and female students. Male
students need to be helped towards a more positive approach to reading, which requires them to see it as
a useful, profitable and enjoyable activity. Teachers need to consider the expectations that they have of
students of both sexes and adopt strategies to raise the levels of self-confidence and motivation of students in
those areas where each are weak. This cannot be achieved simply through classroom practice, since reading
is a cultural practice influenced by the social context. Promoting male reading interest therefore needs to
involve the family and society more widely. In similar respects, females need wide support in developing
their interest and self-regard in mathematics. In particular, female students who do not have confidence in
their mathematical abilities are likely to be constrained in their future choice of career, making it important
to aim to build this aspect of their confidence.
At the same time, the influence of the cultural beliefs prevailing in a country and the effect of the media
have not been considered in this report, but are influences which cannot be ignored. It is possible that by
the time students are reaching the age to make choices for education after secondary school, that these
influences are becoming very important. It was seen in the report that females graduate at a higher rate from
general programmes at secondary school, but are showing definite preferences when entering tertiary level
science education, with much smaller numbers taking up computer sciences compared to life sciences. This
weakness in or aversion to computer studies was also shown, in the three countries that participated, by the
results obtained in the PISA 2006 CBAS project, where females scored significantly lower than males.
The question of whether males and females are better being schooled in single-sex or mixed-sex surroundings
continues to be a vexing one for education authorities around the world. The evidence from PISA does not
support the notion that females tend to do better in a single-sex environment. However, caution is needed
in interpreting these results because of the relatively small numbers of students and because PISA does not
measure either the social environment or the social development of students which is also an important
goal of education.
In a number of previous studies of secondary students it has been found that, in general, females do more
homework than males. The results from PISA 2006 support this observation in all subject areas.
At the same time, PISA has shown that there are, indeed, areas where some of the accepted pre-conceived
notions regarding stereotypic male and female behaviour are simply not true. Females did not score more
highly in life sciences as would have been widely expected. The results from PISA 2006 also demonstrated
the clear advantage that females have in identifying scientific issues.
48
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Notes
1 Further detail on the development and implementation of PISA is provided in the Annex.
2. The Slovak Republic and Turkey did not participate in PISA 2000. while Luxembourg. the Netherlands.
the United Kingdom and the United States were not included in the trend comparison for reasons described
in the PISA 2006 initial report. PISA 2006: Science Competencies For Tomorrow’s World (OECD, 2007).
3. The vast majority of items are retained from one PISA survey to the next to be re-used. so unreleased items
cannot be described here.
4. ESCS was derived from the following variables: the higher occupational status of the father or mother;
the higher educational level of the father or mother; and the index of home possessions obtained by asking
students whether they had at their home various items such as a desk to study at. a room of their own. a
quiet place to study. a computer they can use for school. any educational software. a link to the Internet.
their own calculator. classic literature. a dishwasher. a DVD player or VCR. the number of cellular phones.
televisions. computers. cars and books at home. The rationale for the choice of these variables was that
socio-economic status is usually seen as being determined by occupational status. education and wealth. As
no direct measure on parental income was available from PISA (except for those countries which undertook
the parent questionnaire). access to relevant household items was used as a proxy.
5. In this two-level regression analysis. students serve as Level 1 and schools serve as Level 2. The dependent
variables are the five plausible values in science; independent variables at Level 1 are the gender variable
(0=male and 1=female) and students’ ESCS; an independent variable at Level 2 is school average ESCS; and
the cross-level interaction between gender and school average ESCS is included. Intercepts and slopes for
gender are randomised. Students’ ESCS and school average ESCS are grand-mean centred. Normalised final
students weights as well as normalised replicates are used.
6. For the purpose of this analysis these are those countries in which students with an immigrant background
represent at least 3% of the 15-year-old student population.
7. It should be noted also that much of the research that has been done so far is in the measurement of effects
of single-sex classrooms. rather than single-sex schools. For this PISA is unable to provide any information
because the PISA sample is an age-based sample taking students across classes and indeed across grades.
There is also no teacher questionnaire in PISA to give information on this.
8. This restriction applies to all analyses in PISA.
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49
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51
ppendix A – Background of PISA
APPENDIX A - Background of pisa
The development of PISA surveys
Decisions about the scope and nature of the assessments and the background information to be collected
are made by leading experts in participating countries, with the overall project being steered jointly by
governments on the basis of shared, policy-driven interests. The frameworks for assessing scientific, reading
and mathematical literacy in 2006 are described in full in Assessing Scientific, Reading and Mathematical
Literacy: A Framework for PISA 2006 (OECD, 2006a). Substantial efforts and resources are devoted to
achieving cultural and linguistic breadth and balance in the assessment materials. Stringent quality assurance
mechanisms are applied in translation, sampling and data collection. As a consequence, the results of PISA
have a high degree of validity and reliability.
Although PISA was originally created by the governments of OECD countries, 27 partner countries and
economies participated in PISA 2006 in addition to the 30 OECD countries, making a total of 57 participating
countries.
The PISA student population
PISA covers students who are aged between 15 years 3 months and 16 years 2 months at the time of the
assessment and who have completed at least 6 years of formal schooling, regardless of the type of institution
in which they are enrolled and whether they are in full-time or part-time education, whether they attend
academic or vocational programmes, and whether they attend public or private schools or foreign schools
within the country. The percentage of males and females in the samples in participating countries is shown
in Table A. It can be seen that in most countries the percentages of males and females were very similar.
The largest difference among OECD countries was in the Czech Republic where 56.6% of students in the
sample were male. Among the partner countries and economies the largest differences were in Thailand
(57.4% female) and in Chile (54% male).
In addition to reviewing the gender balance in the overall sample, it is important to consider the response
rates of males and females, Previous analyses (Monseur, 2005) had shown differential response rates for
males and females. In several countries, the difference between male and female response rate was greater
than 2%. For instance, in Portugal, the response rate for males was 82.6% and for females 87.8%. As gender
was found to be correlated with performance, particularly in reading literacy, a student non-response
adjustment was developed for PISA which compensated for differential grade and gender response rates.
All technical details of the design and implementation of PISA are included in the technical reports which
are released after the release of the initial international reports - for an example see PISA 2006 Technical
Report (OECD, 2009).
Key features of PISA 2006
Content
•A
lthough the survey also covered reading and mathematics, the main focus of PISA 2006 was science,
2006 being the first occasion on which science was the major domain.
•T
he PISA 2006 survey also, for the first time, sought information on students’ attitudes to science by
including questions on attitudes within the test itself, rather than only through a complementary
questionnaire.
52
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ppendix A – Background of PISA
Table A Percentage of males and females in each participating country’s sample
Partners
OECD
Males
Females
Percentage
S.E.
Percentage
S.E.
Australia
51
(1.4)
49
(1.4)
Austria
51
(1.8)
49
(1.8)
Belgium
52
(1.4)
48
(1.4)
Canada
50
(0.6)
50
(0.6)
Czech Republic
57
(1.9)
43
(1.9)
Denmark
50
(0.8)
50
(0.8)
Finland
50
(0.8)
50
(0.8)
France
49
(1.3)
51
(1.3)
Germany
52
(0.9)
48
(0.9)
Greece
50
(1.0)
50
(1.0)
Hungary
52
(1.9)
48
(1.9)
Iceland
50
(0.8)
50
(0.8)
Ireland
49
(1.1)
51
(1.1)
Italy
50
(1.0)
50
(1.0)
Japan
50
(2.4)
50
(2.4)
Korea
51
(3.0)
49
(3.0)
Luxembourg
51
(0.7)
49
(0.7)
Mexico
48
(1.0)
52
(1.0)
Netherlands
51
(0.9)
49
(0.9)
New Zealand
48
(2.1)
52
(2.1)
Norway
52
(0.7)
48
(0.7)
Poland
50
(0.7)
50
(0.7)
Portugal
48
(0.8)
52
(0.8)
Slovak Republic
51
(1.7)
49
(1.7)
Spain
51
(0.7)
49
(0.7)
Sweden
51
(0.8)
49
(0.8)
Switzerland
52
(0.8)
48
(0.8)
Turkey
55
(1.9)
45
(1.9)
United Kingdom
50
(1.0)
50
(1.0)
United States
51
(0.9)
49
(0.9)
OECD average
51
(0.2)
49
(0.2)
Argentina
47
(1.4)
53
(1.4)
Azerbaijan
52
(0.9)
48
(0.9)
Brazil
46
(0.8)
54
(0.8)
Bulgaria
52
(1.8)
48
(1.8)
Chile
54
(1.6)
46
(1.6)
Colombia
46
(1.9)
54
(1.9)
Croatia
50
(1.9)
50
(1.9)
Estonia
51
(0.9)
49
(0.9)
Hong Kong-China
49
(1.9)
51
(1.9)
Indonesia
51
(2.1)
49
(2.1)
Israel
50
(1.4)
50
(1.4)
Jordan
50
(1.9)
50
(1.9)
Kyrgyzstan
47
(0.8)
53
(0.8)
Latvia
49
(0.7)
51
(0.7)
Liechtenstein
46
(2.3)
54
(2.3)
Lithuania
51
(0.7)
49
(0.7)
Macao-China
51
(0.8)
49
(0.8)
Montenegro
52
(0.6)
48
(0.6)
Qatar
51
(0.1)
49
(0.1)
Romania
50
(1.8)
50
(1.8)
Russian Federation
48
(1.0)
52
(1.0)
Serbia
51
(1.5)
49
(1.5)
Slovenia
50
(0.7)
50
(0.7)
Chinese Taipei
52
(1.5)
48
(1.5)
Thailand
43
(1.4)
57
(1.4)
Tunisia
48
(0.9)
52
(0.9)
Uruguay
49
(0.9)
51
(0.9)
Source: OECD PISA 2006 Database.
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53
Appendix A – Background of PISA
Methods
•A
round 400 000 students participated in PISA 2006, representing about 20 million 15-year-olds in the
schools of the 57 participating countries and economies.
• E ach participating student spent two hours carrying out pencil-and-paper tasks. In three countries, some
students were given additional questions via computer.
• PISA
contained tasks requiring students to construct their own answers as well as multiple-choice
questions. These were typically organised in units based on a written passage or graphic, of the kind that
students might encounter in real life.
• S tudents also answered a questionnaire that took about 30 minutes to complete and focused on their
personal background, their learning habits and their attitudes to science, as well as on their engagement
and motivation.
• S chool principals completed a questionnaire about their school that included demographic characteristics
as well as an assessment of the quality of the learning environment at school. In 16 countries parents of
the students who participated in PISA also completed a questionnaire.
Outputs
•A
profile of knowledge and skills among 15-year-olds in 2006, consisting of a detailed profile for science,
and an update for reading and mathematics.
•C
ontextual indicators relating performance results to student and school characteristics.
• An assessment of students’ attitudes to science.
•A
knowledge base for policy analysis and research
•T
rend data on changes in student knowledge and skills in reading and mathematics.
The PISA 2006 science assessment framework
The establishment of an assessment in PISA begins with the creation of the assessment framework. The
primary benefit of developing a framework for any assessment is improved measurement. Developing a
framework also improves interpretability, allowing a better understanding of how performances differ. A
framework provides a common language for discussing the definition and assumptions surrounding the
domain. As mentioned in the introductory section of this report, the frameworks for assessing scientific,
reading and mathematical literacy in 2006 are described in full in Assessing Scientific, Reading and
Mathematical Literacy: A Framework for PISA 2006 (OECD, 2006a). Further elaboration of the reading
and mathematics assessment frameworks can be found in Measuring Student Knowledge and Skills: A
New Framework for Assessment (OECD, 1999) and The PISA 2003 Assessment Framework - Mathematics,
Reading, Science and Problem Solving Knowledge and Skills, (OECD, 2003).
In addition to the competencies and knowledge domains (which are described earlier in this report), PISA
frameworks also consider context as an important element. In keeping with the PISA orientation of assessing
students’ preparation for future life, the PISA 2006 science questions were framed within a wide variety of
life situations involving science and technology, namely: “Health”, “Natural resources”, “Environmental
quality”, “Hazards” and “Frontiers of science and technology”. These situations were related to three
major contexts: personal (the self, family and peer groups), social (community) and global (life across the
54
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Appendix A – Background of PISA
world). The contexts used for questions were chosen in the light of relevance to students’ interests and
lives, representing science-related situations that adults encounter. Almost daily, adults hear about and face
decisions concerning health, use of resources, environmental quality, hazard mitigation, and advances in
science and technology. The science contexts also align with various issues policy makers confront.
Development of the science items in PISA 2006
PISA items are arranged in units based around a common stimulus. Many different types of stimulus are
used including passages of text, tables, graphs and diagrams, often in combination. Each unit contains
up to four items assessing students’ scientific competencies and knowledge. In addition, for PISA 2006
about 60% of the science units contained one or two items designed to assess aspects of students’ attitudes
towards science. The terms “cognitive items” and “attitudinal items” are used to distinguish these two
separate types of items.
There were 37 science units, comprising a total of 108 cognitive items and 31 embedded attitudinal items,
representing approximately 210 minutes of testing time for science in PISA 2006. The same amount of time
was allocated to the major domain for 2003 (mathematics), although there were no attitudinal items in the
2003 assessment.
The 108 science cognitive items used in the main study included 22 items from the 2003 assessment. The
remaining 86 items were selected from a large pool of newly-developed items that had been tested in a field
trial conducted in all countries in 2005, one year prior to the main study.
There were four item formats employed for the science cognitive items: simple multiple-choice, complex
multiple-choice, short-response, and open-constructed response. The simple multiple-choice items had four
responses from which students were required to select the best answer while complex multiple-choice
items presented several statements for each of which students were required to choose one of two possible
responses (yes/no, true/false, correct/incorrect, etc.). Short-response items required students to construct a
numeric response within very limited constraints, or only required a word or short phrase as the answer.
Open-constructed response items required more extensive writing than short-response items and frequently
required some explanation or justification. In the past cycles of PISA a relationship between gender and
item type had been identified. Each attitudinal item required students to express their level of agreement on
a four-point scale with two or three statements expressing either interest in science or support for science.
Each attitudinal item was formatted distinctively and appeared in a shaded box.
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55
Appendix B – dATA TABLES
Table 1 Upper secondary graduation rates (2006)
Percentage of upper secondary graduates in the population at the typical age of graduation. programme orientation
and gender
OECD
Total (unduplicated)
M+F
Males
M+F
Males
Females
M+F
Males
Females
Australia
m
m
m
68.5
62.9
74.2
40.6
36.7
44.7
Austria
m
m
m
16.6
13.2
20.1
50.0
61.4
38.1
Belgium
m
m
m
36.8
31.3
42.6
57.7
55.8
59.7
Canada
80.2
76.6
84.0
77.3
73.1
81.7
7.8
8.5
7.0
Czech Republic
89.7
87.6
92.0
18.2
13.5
23.1
72.1
74.8
69.4
Denmark
86.5
77.9
95.5
55.0
44.5
66.1
50.8
45.9
55.9
Finland
95.4
90.9
100.1
51.4
42.5
60.8
88.0
79.6
96.7
France1
m
m
m
50.9
43.3
58.7
62.5
64.8
60.1
Germany
102.7
101.7
103.7
39.9
35.4
44.5
62.8
66.3
59.1
Greece
100.1
96.3
104.1
63.2
55.1
71.9
34.7
38.7
30.4
Hungary
85.4
81.0
89.9
69.8
62.5
77.3
18.4
22.4
14.4
Iceland
90.4
80.7
99.7
65.5
55.1
75.5
54.9
55.7
54.0
Ireland
86.5
80.7
92.5
62.5
60.5
64.6
52.8
36.7
69.4
Italy
85.5
83.6
87.6
31.3
21.9
41.3
69.3
75.8
62.4
Japan
92.6
92.0
93.3
69.5
66.5
72.7
23.1
25.5
20.6
Korea
93.1
92.2
94.0
66.4
65.5
67.4
26.6
26.7
26.6
Luxembourg
71.6
69.0
74.3
27.5
22.6
32.6
44.1
46.5
41.5
Mexico
42.0
38.5
45.5
38.1
34.6
41.6
3.9
3.9
4.0
m
m
m
35.5
32.4
38.9
66.0
64.9
67.2
New Zealand
73.7
62.5
85.3
m
m
m
m
m
m
Norway
91.4
80.4
102.9
55.7
44.0
67.9
42.1
44.0
40.1
Poland
80.1
76.2
84.1
59.2
48.6
70.2
35.7
44.7
26.4
m
m
m
40.4
30.7
50.4
13.2
15.4
13.2
Slovak Republic
82.4
80.3
84.6
22.8
18.2
27.6
69.1
73.0
65.1
Spain
72.0
64.2
80.3
44.8
36.9
53.2
34.9
32.5
37.6
Sweden
75.7
72.7
78.9
33.9
28.2
39.9
41.8
44.5
39.0
Switzerland
89.5
90.1
88.8
29.8
25.5
34.2
68.7
74.6
62.4
Turkey
51.2
54.7
47.5
35.2
35.8
34.6
19.5
22.8
16.0
United Kingdom
88.3
85.3
91.5
m
m
m
m
m
m
United States
77.2
75.3
79.3
m
m
m
m
m
m
OECD average
82.6
78.8
86.6
46.9
40.9
53.1
44.9
46.9
43.7
Netherlands
Portugal
Partners
Pre-vocational/ vocational
programmes
General programmes
Brazil1
Females
m
m
m
62.3
53.2
71.9
7.9
5.9
10.0
Chile
70.9
66.6
75.4
38.8
34.7
42.9
32.2
31.9
32.5
Estonia
75.5
68.0
83.4
58.2
45.4
71.6
17.8
22.9
12.4
Israel
90.0
87.8
92.3
57.6
52.2
63.3
32.4
35.6
29.0
m
m
m
55.6
x
x
35.7
x
x
96.8
88.6
105.3
34.4
26.1
43.0
79.3
80.0
78.6
Russian Federation
Slovenia
Note: Mismatches between the coverage of the population data and the student/graduate data mean that the participation/graduation rates for those countries that are net exporters of students may be underestimated (for instance Luxembourg) and those that are net importers may be overestimated.
1. Year of reference 2005.
Source: Education at a Glance - OECD Indicators 2008 (OECD, 2008).
56
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
OECD
Table 2 Proportion of females among new entrants at tertiary level. by field of education (2006)
Life sciences
%
Physical sciences
%
Mathematics and statistics
%
Computing
%
Australia
53.7
45.6
34.5
17.3
Austria
65.5
30.8
37.7
18.8
Belgium
55.9
31.9
44.3
7.4
Canada
m
m
m
m
Czech Republic
69.4
46.4
48.4
15.8
Denmark
61.9
40.1
40.5
30.8
Finland
75.2
50.5
50.4
28.1
France
m
m
m
m
68.4
43.5
58.3
17.6
Germany
Greece
m
m
m
m
Hungary
61.2
37.7
39.1
20.2
Iceland
73.5
45.9
35.5
13.8
Ireland
69.6
49.3
46.9
24.4
Italy
67.0
41.6
48.5
13.2
Japan
x
x
x
x
Korea
49.4
46.0
56.0
23.7
Luxembourg
m
m
m
m
Mexico
54.9
49.4
42.1
34.4
Netherlands
55.3
27.6
27.0
9.3
New Zealand
62.4
47.1
40.5
29.2
Norway
64.2
44.5
45.5
17.7
Poland
62.0
53.5
57.3
8.6
Portugal
68.0
51.2
54.1
17.6
Slovak Republic
m
m
m
m
Spain
63.8
45.0
48.5
14.2
Sweden
59.8
43.8
43.1
20.6
Switzerland
50.8
31.0
34.1
12.8
Turkey
53.4
37.3
44.0
31.6
United Kingdom
49.7
42.5
38.4
25.1
m
m
m
m
61.5
42.7
44.1
19.7
United States
Partners
OECD average
Brazil
m
m
m
m
Chile
53.0
48.1
42.5
14.4
Estonia
72.9
35.9
70.1
22.6
Israel
62.7
35.1
26.9
x
m
m
m
m
76.2
52.2
66.8
13.3
Russian Federation
Slovenia
Source: Education at a Glance - OECD Indicators 2008 (OECD, 2008).
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
57
Appendix B – dATA TABLES
Table 3 Categorisation of readers (PISA 2000)
Cluster
Percentage of males
Percentage of females
Reading profile
Least diversified readers
20.9
23.6
Little diversification in reading. only frequently reading
magazines. Small percentages reading fiction. Do not
read for pleasure.
Moderately diversified readers
29.8
24.7
Majority of students frequently read magazines and
newspapers. rarely read any type of book (fiction or
non-fiction). almost never read comics.
Diversified readers in short texts
33.8
22.9
Majority frequently read magazines. newspapers and
comics. Moderate readers of fiction and non-fiction.
Diversified but focus on short. non-demanding texts.
Diversified readers in long texts
15.5
28.8
Majority frequently read magazines. newspapers.
fiction and to a lesser extent non-fiction books. Very
small percentage read comics frequently.
Source: Reading for Change - Performance and Engagement across Countries: Results from PISA 2000 (OECD, 2002).
58
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 4 Gender differences (males - females) in standard deviation for science, mathematics and reading (PISA 2006)
Science
Partners
OECD
Dif.
Mathematics
S.E.
Dif.
S.E.
Identifying
scientific issues
Reading
Dif.
S.E.
Dif.
S.E.
Explaining phenoma
scientifically
Dif.
S.E.
Using scientific
evidence
Dif.
S.E.
Australia
7.3
(1.9)
7.2
(1.9)
9.6
(2.0)
7.6
(2.0)
6.4
(1.8)
6.9
(2.2)
Austria
-0.4
(3.6)
0.9
(3.1)
6.1
(6.1)
1.2
(3.6)
2.2
(3.4)
-2.0
(5.4)
Belgium
6.8
(2.5)
10.1
(3.9)
11.4
(3.3)
7.3
(3.1)
7.0
(2.4)
7.5
(2.9)
Canada
5.9
(1.6)
4.8
(1.5)
8.2
(1.7)
5.7
(1.7)
5.0
(1.4)
6.2
(1.6)
Czech Republic
-4.1
(2.8)
-3.7
(3.0)
2.5
(4.3)
-1.2
(3.6)
-3.1
(2.6)
-3.8
(3.3)
Denmark
2.9
(2.0)
-0.6
(2.2)
4.0
(2.2)
3.8
(2.3)
2.6
(2.0)
2.5
(2.5)
Finland
7.9
(1.9)
6.2
(2.0)
7.5
(2.0)
8.3
(2.2)
7.8
(2.3)
8.3
(2.6)
France
8.9
(2.7)
6.9
(2.3)
10.9
(2.8)
8.9
(2.7)
8.7
(2.3)
10.0
(2.6)
Germany
5.9
(2.2)
3.4
(2.5)
8.2
(2.9)
6.5
(2.5)
5.9
(2.5)
6.3
(3.0)
Greece
12.9
(2.9)
10.4
(3.1)
20.4
(3.8)
11.8
(3.1)
11.7
(3.1)
16.9
(4.1)
Hungary
8.5
(2.9)
9.5
(2.9)
9.6
(3.0)
6.6
(2.7)
8.7
(2.8)
9.0
(3.2)
Iceland
7.5
(2.2)
7.1
(2.0)
12.2
(2.4)
9.1
(2.9)
8.0
(2.7)
7.6
(2.8)
Ireland
7.1
(2.1)
5.7
(2.3)
7.7
(2.4)
6.5
(2.4)
7.5
(2.3)
7.7
(2.4)
Italy
9.0
(2.1)
9.6
(2.1)
12.2
(2.4)
10.4
(2.1)
9.6
(2.2)
9.4
(2.6)
Japan
8.7
(3.3)
6.8
(3.3)
12.1
(3.6)
6.7
(4.5)
9.7
(2.7)
8.1
(4.2)
Korea
7.4
(2.7)
7.9
(3.2)
8.1
(3.0)
8.0
(3.1)
7.0
(2.6)
8.9
(3.0)
Luxembourg
8.7
(2.1)
5.4
(2.0)
7.2
(2.2)
6.5
(2.2)
8.1
(2.1)
9.8
(2.4)
Mexico
3.1
(1.8)
4.8
(1.7)
5.2
(2.1)
0.7
(1.6)
3.5
(1.5)
3.1
(1.9)
Netherlands
2.8
(2.4)
1.0
(2.0)
5.3
(2.7)
0.6
(2.3)
3.0
(2.6)
0.2
(2.5)
New Zealand
8.6
(2.4)
8.2
(2.4)
8.7
(2.7)
6.8
(2.5)
9.3
(2.8)
9.3
(3.0)
Norway
9.3
(2.4)
8.9
(2.1)
13.3
(3.0)
8.7
(2.2)
9.6
(2.2)
8.8
(2.3)
Poland
7.0
(1.8)
5.4
(1.8)
10.9
(2.6)
6.3
(1.5)
6.5
(1.8)
8.1
(1.8)
Portugal
4.9
(2.1)
6.0
(2.1)
6.7
(2.7)
3.6
(2.4)
3.9
(2.1)
6.4
(2.5)
Slovak Republic
6.1
(2.9)
3.8
(3.2)
8.5
(3.4)
4.4
(4.1)
7.6
(3.1)
8.4
(3.7)
Spain
6.1
(1.7)
7.5
(1.9)
8.9
(2.2)
8.0
(2.0)
4.8
(2.1)
8.6
(2.1)
Sweden
4.9
(2.9)
2.6
(2.5)
6.7
(2.7)
4.1
(2.9)
5.3
(2.5)
6.9
(3.3)
Switzerland
0.7
(1.7)
0.4
(2.0)
3.1
(1.7)
0.7
(1.7)
-0.2
(1.8)
1.1
(2.0)
Turkey
4.6
(2.3)
7.0
(2.7)
10.2
(3.2)
3.0
(2.5)
3.5
(2.5)
7.9
(2.8)
United Kingdom
9.4
(2.2)
7.4
(2.0)
11.0
(2.2)
8.7
(2.0)
9.7
(2.2)
8.3
(2.5)
United States
7.1
(2.4)
3.6
(1.8)
m
m
7.3
(2.5)
8.9
(2.8)
8.5
(2.9)
OECD average
6.2
(0.4)
5.5
(0.4)
8.8
(0.5)
5.9
(0.5)
6.3
(0.4)
6.8
(0.5)
Argentina
0.7
(3.3)
-4.5
(3.7)
8.1
(4.5)
1.1
(3.4)
0.2
(3.8)
1.7
(3.2)
Azerbaijan
3.0
(1.4)
-1.5
(2.4)
3.8
(2.0)
1.4
(2.0)
3.7
(1.5)
5.2
(1.6)
Brazil
4.4
(2.1)
2.8
(2.3)
6.1
(2.2)
3.5
(2.5)
4.6
(2.3)
3.2
(2.1)
Bulgaria
6.0
(3.0)
8.6
(3.1)
10.7
(3.4)
5.3
(2.8)
7.3
(2.9)
8.1
(3.7)
Chile
2.1
(3.2)
3.0
(2.9)
7.1
(3.3)
1.1
(2.8)
2.1
(3.2)
1.9
(2.9)
Colombia
6.6
(2.5)
4.6
(3.2)
4.0
(4.0)
6.4
(3.1)
8.2
(2.5)
4.3
(2.8)
Croatia
5.8
(2.0)
8.2
(2.3)
8.0
(2.5)
3.9
(2.1)
5.2
(2.2)
7.2
(2.5)
Estonia
5.7
(1.8)
7.7
(1.9)
7.5
(2.0)
4.6
(1.7)
5.2
(2.2)
7.8
(2.0)
Hong Kong-China
7.1
(2.6)
5.2
(2.9)
5.7
(2.4)
5.2
(3.4)
8.7
(2.5)
8.6
(3.0)
Indonesia
5.4
(3.3)
6.5
(3.2)
5.9
(3.0)
3.2
(3.0)
2.4
(3.1)
7.2
(4.4)
Israel
13.0
(3.3)
14.2
(4.8)
16.9
(4.2)
11.9
(3.1)
12.0
(3.0)
14.3
(3.7)
Jordan
13.1
(3.2)
14.1
(3.1)
19.9
(3.6)
10.5
(3.6)
13.4
(3.1)
18.0
(3.6)
Kyrgyzstan
7.3
(2.4)
7.4
(2.3)
7.8
(2.1)
4.3
(2.8)
8.8
(2.7)
9.3
(2.5)
Latvia
3.3
(2.2)
3.2
(2.4)
6.0
(3.8)
4.0
(2.5)
3.5
(2.6)
5.5
(2.6)
Liechtenstein
-0.1
(7.9)
-3.4
(7.9)
-1.9
(8.3)
-0.8
(6.8)
2.3
(7.5)
-2.0
(7.9)
Lithuania
2.2
(1.9)
2.1
(2.3)
4.0
(2.2)
1.9
(2.4)
1.5
(2.3)
4.5
(2.1)
Macao-China
8.3
(1.6)
7.3
(1.8)
10.1
(2.5)
7.5
(2.0)
8.5
(2.2)
8.9
(2.1)
Montenegro
2.0
(2.0)
1.3
(2.1)
6.0
(2.4)
0.4
(2.4)
3.1
(2.1)
2.1
(2.2)
Qatar
9.8
(1.6)
15.3
(2.2)
10.4
(2.1)
8.4
(1.5)
11.0
(1.6)
12.1
(1.9)
Romania
8.4
(2.4)
7.4
(2.3)
3.8
(3.5)
4.1
(2.3)
10.0
(2.9)
10.8
(3.8)
Russian Federation
6.6
(1.8)
4.9
(2.0)
7.4
(2.2)
5.6
(2.0)
6.5
(2.1)
8.7
(2.4)
Serbia
3.6
(2.1)
3.4
(2.3)
3.1
(2.3)
4.8
(2.2)
4.8
(2.3)
4.6
(2.5)
Slovenia
4.8
(2.5)
4.3
(2.3)
12.2
(1.4)
4.9
(2.1)
6.7
(2.3)
3.6
(2.1)
Chinese Taipei
2.6
(2.6)
2.9
(3.1)
5.1
(2.8)
3.6
(2.7)
2.8
(2.8)
5.0
(2.8)
Thailand
9.3
(2.1)
7.2
(2.3)
11.8
(2.5)
8.8
(2.1)
8.5
(1.9)
10.3
(2.3)
Tunisia
2.3
(2.4)
3.3
(2.7)
9.2
(3.2)
0.4
(3.1)
3.6
(2.9)
3.1
(3.2)
Uruguay
8.2
(3.1)
3.4
(2.9)
8.4
(3.2)
6.5
(2.9)
6.9
(3.2)
8.4
(2.9)
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
59
Appendix B – dATA TABLES
Table 5 [Part 1/2] Percentage correct for males and females for each science item (PISA 2006)
Percentage of correct answers for
All students
Males
Females
Item ID
Science competency
Question type
%
S.E.
%
S.E.
%
S.E.
S114Q03
Using scientific evidence
Open response
53.9
(0.26)
52.8
(0.36)
55.1
(0.36)
S114Q04
Using scientific evidence
Open response
34.5
(0.21)
34.6
(0.29)
34.3
(0.29)
S114Q05
Explaining phenomena scientifically
Open response
18.9
(0.19)
18.7
(0.26)
19.1
(0.27)
S131Q02
Using scientific evidence
Open response
46.2
(0.26)
45.2
(0.35)
47.3
(0.36)
S131Q04
Identifying scientific issues
Open response
31.1
(0.23)
29.7
(0.30)
32.6
(0.32)
S213Q01
Identifying scientific issues
Complex Multiple Choice
47.9
(0.26)
45.1
(0.35)
50.7
(0.35)
S213Q02
Explaining phenomena scientifically
Multiple Choice
79.4
(0.20)
81.7
(0.26)
77.0
(0.29)
S256Q01
Explaining phenomena scientifically
Multiple Choice
87.8
(0.16)
86.4
(0.24)
89.2
(0.20)
S268Q01
Identifying scientific issues
Multiple Choice
72.5
(0.22)
71.0
(0.31)
74.0
(0.30)
S268Q02
Explaining phenomena scientifically
Open response
36.2
(0.24)
35.5
(0.33)
36.9
(0.34)
S268Q06
Explaining phenomena scientifically
Multiple Choice
55.2
(0.25)
58.8
(0.34)
51.5
(0.35)
S269Q01
Explaining phenomena scientifically
Open response
57.8
(0.25)
61.0
(0.34)
54.5
(0.34)
S269Q03
Explaining phenomena scientifically
Open response
41.2
(0.25)
44.0
(0.34)
38.4
(0.34)
S269Q04
Explaining phenomena scientifically
Complex Multiple Choice
34.1
(0.23)
40.8
(0.33)
27.1
(0.30)
S304Q01
Using scientific evidence
Open response
43.6
(0.25)
44.9
(0.34)
42.3
(0.34)
S304Q02
Explaining phenomena scientifically
Multiple Choice
62.1
(0.25)
65.4
(0.33)
58.7
(0.33)
S304Q03a
Using scientific evidence
Open response
39.0
(0.24)
38.2
(0.34)
39.8
(0.34)
S304Q03b
Explaining phenomena scientifically
Open response
50.7
(0.26)
51.6
(0.36)
49.7
(0.34)
S326Q01
Using scientific evidence
Open response
59.0
(0.24)
56.2
(0.34)
62.0
(0.33)
S326Q02
Using scientific evidence
Open response
63.7
(0.25)
61.0
(0.34)
66.6
(0.33)
S326Q03
Using scientific evidence
Multiple Choice
58.3
(0.25)
57.4
(0.33)
59.1
(0.35)
S326Q04
Explaining phenomena scientifically
Complex Multiple Choice
23.3
(0.22)
23.5
(0.29)
23.2
(0.29)
S408Q01
Explaining phenomena scientifically
Multiple Choice
62.9
(0.23)
64.1
(0.32)
61.8
(0.31)
S408Q03
Explaining phenomena scientifically
Open response
30.5
(0.23)
29.8
(0.31)
31.2
(0.32)
S408Q04
Explaining phenomena scientifically
Complex Multiple Choice
50.7
(0.24)
49.8
(0.34)
51.7
(0.34)
S408Q05
Identifying scientific issues
Multiple Choice
42.0
(0.24)
42.5
(0.34)
41.4
(0.33)
S413Q04
Using scientific evidence
Complex Multiple Choice
41.4
(0.25)
43.2
(0.34)
39.6
(0.34)
S413Q05
Using scientific evidence
Multiple Choice
65.6
(0.24)
66.1
(0.33)
65.0
(0.33)
S413Q06
Explaining phenomena scientifically
Closed Constructed Response
37.8
(0.26)
40.7
(0.36)
34.9
(0.35)
S415Q02
Explaining phenomena scientifically
Multiple Choice
78.3
(0.21)
77.8
(0.30)
78.9
(0.28)
S415Q07
Identifying scientific issues
Complex Multiple Choice
72.1
(0.23)
70.0
(0.32)
74.2
(0.30)
S415Q08
Identifying scientific issues
Complex Multiple Choice
57.7
(0.25)
57.0
(0.35)
58.4
(0.34)
S416Q01
Using scientific evidence
Closed Constructed Response
45.4
(0.25)
46.8
(0.35)
44.1
(0.34)
S421Q01
Explaining phenomena scientifically
Closed Constructed Response
39.8
(0.26)
41.8
(0.35)
37.9
(0.35)
S421Q02
Explaining phenomena scientifically
Closed Constructed Response
0.0
(0.00)
0.0
(0.00)
0.0
(0.00)
S421Q03
Explaining phenomena scientifically
Closed Constructed Response
63.0
(0.25)
69.0
(0.32)
56.9
(0.35)
S425Q02
Using scientific evidence
Multiple Choice
45.8
(0.25)
47.8
(0.34)
43.8
(0.34)
S425Q03
Explaining phenomena scientifically
Open response
41.4
(0.25)
42.9
(0.34)
40.0
(0.34)
S425Q04
Using scientific evidence
Open response
30.1
(0.23)
28.1
(0.31)
32.2
(0.32)
S425Q05
Identifying scientific issues
Multiple Choice
69.0
(0.23)
66.8
(0.32)
71.2
(0.31)
S426Q01
Explaining phenomena scientifically
Open response
0.0
(0.00)
0.0
(0.00)
0.0
(0.00)
S426Q03
Explaining phenomena scientifically
Multiple Choice
67.6
(0.24)
68.3
(0.32)
66.9
(0.33)
S426Q05
Explaining phenomena scientifically
Multiple Choice
75.8
(0.22)
76.9
(0.30)
74.6
(0.31)
S426Q07
Identifying scientific issues
Complex Multiple Choice
61.3
(0.23)
59.9
(0.34)
62.8
(0.32)
S428Q01
Using scientific evidence
Multiple Choice
61.7
(0.24)
64.0
(0.33)
59.3
(0.34)
S428Q03
Using scientific evidence
Multiple Choice
71.3
(0.24)
71.3
(0.33)
71.4
(0.33)
S428Q05
Explaining phenomena scientifically
Open response
43.9
(0.26)
43.9
(0.35)
43.8
(0.35)
S437Q01
Explaining phenomena scientifically
Multiple Choice
72.2
(0.23)
75.4
(0.30)
68.9
(0.33)
S437Q03
Explaining phenomena scientifically
Multiple Choice
49.4
(0.26)
52.7
(0.34)
45.9
(0.35)
S437Q04
Explaining phenomena scientifically
Multiple Choice
58.0
(0.24)
60.6
(0.32)
55.3
(0.34)
S437Q06
Explaining phenomena scientifically
Open response
76.0
(0.22)
78.0
(0.29)
73.9
(0.31)
S438Q01
Identifying scientific issues
Complex Multiple Choice
83.2
(0.19)
81.4
(0.27)
85.1
(0.25)
S438Q02
Identifying scientific issues
Multiple Choice
65.6
(0.24)
63.1
(0.34)
68.1
(0.33)
S438Q03
Identifying scientific issues
Open response
38.9
(0.25)
36.1
(0.34)
41.8
(0.34)
Source: OECD PISA 2006 Student Compendium.
60
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 5 [Part 2/2] Percentage correct for males and females for each science item (PISA 2006)
Percentage of correct answers for
All students
Males
Females
Item ID
Science competency
Question type
%
S.E.
%
S.E.
%
S.E.
S447Q02
Identifying scientific issues
Multiple Choice
40.5
(0.24)
38.1
(0.32)
43.0
(0.34)
S447Q03
Identifying scientific issues
Multiple Choice
58.3
(0.23)
56.3
(0.31)
60.3
(0.33)
S447Q04
Identifying scientific issues
Multiple Choice
43.0
(0.24)
41.7
(0.33)
44.3
(0.34)
S447Q05
Using scientific evidence
Open response
27.1
(0.22)
25.8
(0.30)
28.4
(0.32)
S456Q01
Explaining phenomena scientifically
Complex Multiple Choice
0.0
(0.00)
0.0
(0.00)
0.0
(0.00)
S456Q02
Explaining phenomena scientifically
Multiple Choice
0.0
(0.00)
0.0
(0.00)
0.0
(0.00)
S458Q01
Explaining phenomena scientifically
Open response
16.3
(0.19)
18.2
(0.27)
14.3
(0.24)
S458Q02
Using scientific evidence
Complex Multiple Choice
56.2
(0.25)
54.2
(0.35)
58.3
(0.35)
S465Q01
Using scientific evidence
Open response
50.2
(0.22)
50.5
(0.30)
49.9
(0.31)
S465Q02
Explaining phenomena scientifically
Multiple Choice
60.9
(0.24)
60.8
(0.34)
61.0
(0.34)
S465Q04
Explaining phenomena scientifically
Multiple Choice
36.3
(0.24)
38.6
(0.34)
33.8
(0.32)
S466Q01
Identifying scientific issues
Complex Multiple Choice
71.0
(0.22)
69.3
(0.32)
72.7
(0.30)
S466Q05
Using scientific evidence
Multiple Choice
55.7
(0.24)
58.3
(0.33)
53.0
(0.35)
S466Q07
Identifying scientific issues
Complex Multiple Choice
74.9
(0.22)
71.5
(0.31)
78.3
(0.29)
S476Q01
Explaining phenomena scientifically
Multiple Choice
70.7
(0.24)
72.5
(0.31)
68.8
(0.33)
S476Q02
Explaining phenomena scientifically
Multiple Choice
70.9
(0.22)
74.3
(0.29)
67.2
(0.32)
S476Q03
Explaining phenomena scientifically
Multiple Choice
60.1
(0.25)
62.8
(0.34)
57.3
(0.33)
S477Q02
Explaining phenomena scientifically
Multiple Choice
74.9
(0.22)
72.6
(0.31)
77.2
(0.30)
S477Q03
Explaining phenomena scientifically
Multiple Choice
75.1
(0.22)
75.3
(0.30)
74.9
(0.30)
S477Q04
Explaining phenomena scientifically
Open response
61.7
(0.25)
58.5
(0.33)
65.0
(0.33)
S478Q01
Explaining phenomena scientifically
Multiple Choice
42.8
(0.23)
42.0
(0.33)
43.7
(0.33)
S478Q02
Using scientific evidence
Complex Multiple Choice
51.0
(0.25)
52.2
(0.34)
49.7
(0.35)
S478Q03
Explaining phenomena scientifically
Complex Multiple Choice
67.7
(0.23)
65.2
(0.33)
70.3
(0.31)
S485Q02
Explaining phenomena scientifically
Open response
57.7
(0.26)
59.8
(0.35)
55.5
(0.36)
S485Q03
Using scientific evidence
Multiple Choice
66.7
(0.25)
68.3
(0.33)
65.1
(0.34)
S485Q05
Identifying scientific issues
Open response
35.5
(0.18)
33.5
(0.25)
37.6
(0.25)
S493Q01
Explaining phenomena scientifically
Complex Multiple Choice
52.6
(0.25)
51.9
(0.34)
53.5
(0.34)
S493Q03
Explaining phenomena scientifically
Complex Multiple Choice
82.4
(0.18)
81.7
(0.26)
83.1
(0.24)
S493Q05
Explaining phenomena scientifically
Open response
45.1
(0.25)
49.3
(0.34)
40.8
(0.34)
S495Q01
Using scientific evidence
Complex Multiple Choice
42.1
(0.24)
39.6
(0.33)
44.8
(0.34)
S495Q02
Using scientific evidence
Complex Multiple Choice
57.6
(0.25)
60.9
(0.35)
54.2
(0.34)
S495Q03
Using scientific evidence
Open response
38.6
(0.26)
38.8
(0.35)
38.3
(0.36)
S495Q04
Identifying scientific issues
Complex Multiple Choice
50.2
(0.25)
48.0
(0.33)
52.5
(0.35)
S498Q02
Identifying scientific issues
Complex Multiple Choice
46.9
(0.24)
47.7
(0.35)
46.2
(0.34)
S498Q03
Identifying scientific issues
Multiple Choice
42.6
(0.24)
41.5
(0.33)
43.8
(0.34)
S498Q04
Using scientific evidence
Open response
59.9
(0.25)
57.1
(0.34)
62.8
(0.32)
S508Q02
Identifying scientific issues
Complex Multiple Choice
60.9
(0.23)
58.5
(0.34)
63.5
(0.33)
S508Q03
Identifying scientific issues
Multiple Choice
73.6
(0.23)
72.7
(0.32)
74.4
(0.31)
S508Q04
Identifying scientific issues
Open response
0.0
(0.00)
0.0
(0.00)
0.0
(0.00)
S510Q01
Explaining phenomena scientifically
Complex Multiple Choice
53.9
(0.23)
57.8
(0.33)
49.9
(0.33)
S510Q04
Explaining phenomena scientifically
Open response
41.0
(0.24)
45.4
(0.34)
36.5
(0.33)
S514Q02
Using scientific evidence
Open response
85.2
(0.20)
85.6
(0.26)
84.8
(0.27)
S514Q03
Explaining phenomena scientifically
Open response
46.6
(0.25)
48.9
(0.36)
44.3
(0.34)
S514Q04
Using scientific evidence
Complex Multiple Choice
52.2
(0.27)
49.7
(0.37)
54.7
(0.35)
S519Q01
Using scientific evidence
Open response
35.3
(0.21)
33.3
(0.28)
37.3
(0.30)
S519Q02
Explaining phenomena scientifically
Complex Multiple Choice
52.6
(0.25)
54.4
(0.35)
50.8
(0.34)
S519Q03
Identifying scientific issues
Open response
28.7
(0.22)
27.5
(0.30)
30.0
(0.30)
S521Q02
Explaining phenomena scientifically
Multiple Choice
55.9
(0.24)
58.6
(0.33)
53.1
(0.34)
S521Q06
Explaining phenomena scientifically
Multiple Choice
88.1
(0.18)
86.8
(0.25)
89.5
(0.23)
S524Q06
Using scientific evidence
Complex Multiple Choice
64.3
(0.24)
64.4
(0.33)
64.1
(0.33)
S524Q07
Using scientific evidence
Open response
36.5
(0.24)
37.3
(0.33)
35.7
(0.32)
S527Q01
Using scientific evidence
Complex Multiple Choice
16.1
(0.18)
17.6
(0.26)
14.6
(0.23)
S527Q03
Explaining phenomena scientifically
Complex Multiple Choice
58.0
(0.25)
59.0
(0.34)
56.9
(0.34)
S527Q04
Explaining phenomena scientifically
Complex Multiple Choice
53.7
(0.24)
54.8
(0.34)
52.6
(0.34)
Source: OECD PISA 2006 Student Compendium.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
61
Appendix B – dATA TABLES
Table 6 Results from computer based assessment of science (CBAS) (PISA 2006)
Males
Females
Difference (M-F)
Mean
S.E.
Mean
S.E.
Dif.
S.E.
Denmark
486
(6.2)
440
(7.4)
45
(8.8)
Iceland
485
(2.5)
459
(2.2)
25
(3.4)
Korea
516
(6.5)
490
(7.2)
26
(9.8)
Denmark
492
(7.0)
469
(8.2)
23
(9.0)
Iceland
467
(2.6)
474
(2.5)
-7
(3.8)
Korea
502
(6.2)
503
(6.4)
-1
(9.2)
Denmark
500
(3.6)
491
(3.4)
9
(3.2)
Iceland
488
(2.6)
494
(2.1)
-6
(3.4)
Korea
521
(4.8)
523
(3.9)
-2
(5.5)
CBAS
Scientific literacy (CBAS sample)
Scientific literacy (full sample)
Source: OECD PISA 2006 Computer Based Assessment Database.
62
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 7 PISA index of economic, social and cultural status (ESCS) for males and females (PISA 2006)
Difference
(M - F)
Males
Difference
(M - F)
Females
Mean
S.E.
Mean
S.E.
Dif.
S.E.
Score point
difference
associated
with one
standard
deviation
change on the
ESCS
OECD
Females
Australia
Austria
Belgium
Canada
Switzerland
Czech Republic
Germany
Denmark
Spain
Finland
France
Greece
Hungary
Ireland
Iceland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
Norway
New Zealand
Poland
Portugal
Slovak Republic
Sweden
Turkey
United Kingdom
United States
OECD average
0.21
0.20
0.20
0.35
0.09
0.05
0.33
0.33
-0.28
0.26
-0.07
-0.10
-0.07
-0.01
0.81
-0.02
-0.00
0.01
0.14
-0.99
0.29
0.45
0.10
-0.25
-0.57
-0.12
0.27
-1.33
0.22
0.12
0.02
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.04)
(0.03)
(0.03)
(0.02)
(0.03)
(0.04)
(0.04)
(0.04)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.05)
(0.03)
(0.02)
(0.03)
(0.02)
(0.06)
(0.03)
(0.02)
(0.05)
(0.02)
(0.04)
(0.03)
0.21
0.19
0.15
0.38
0.08
0.01
0.25
0.29
-0.34
0.26
-0.11
-0.20
-0.10
-0.02
0.72
-0.12
-0.02
-0.02
0.03
-0.99
0.22
0.39
0.10
-0.35
-0.66
-0.18
0.20
-1.21
0.16
0.15
-0.02
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.03)
(0.03)
(0.03)
(0.04)
(0.02)
(0.03)
(0.04)
(0.03)
(0.03)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.05)
(0.03)
(0.02)
(0.03)
(0.03)
(0.04)
(0.03)
(0.02)
(0.05)
(0.02)
(0.04)
(0.03)
-0.00
0.01
0.05
-0.03
0.00
0.04
0.08
0.04
0.05
-0.00
0.04
0.10
0.03
0.00
0.09
0.11
0.01
0.03
0.11
0.01
0.07
0.07
0.00
0.10
0.09
0.06
0.07
-0.13
0.05
-0.04
0.04
(0.03)
(0.04)
(0.04)
(0.03)
(0.03)
(0.04)
(0.05)
(0.04)
(0.05)
(0.03)
(0.04)
(0.06)
(0.05)
(0.05)
(0.03)
(0.03)
(0.03)
(0.05)
(0.03)
(0.07)
(0.04)
(0.03)
(0.04)
(0.03)
(0.07)
(0.05)
(0.03)
(0.07)
(0.03)
(0.06)
(0.04)
42.82
39.74
47.77
33.09
44.44
43.51
46.23
37.33
31.87
31.56
54.94
39.99
46.85
39.74
27.69
29.20
37.98
30.38
41.35
25.79
43.08
35.79
53.00
40.35
27.86
44.46
37.13
30.43
48.83
49.21
39.41
(2.50)
(3.17)
(2.44)
(2.12)
(2.00)
(2.83)
(2.69)
(2.68)
(1.75)
(2.59)
(3.25)
(3.05)
(2.69)
(2.91)
(2.98)
(2.24)
(3.45)
(4.11)
(1.92)
(1.53)
(2.94)
(3.01)
(2.67)
(2.11)
(1.74)
(3.20)
(2.57)
(3.40)
(2.40)
(3.11)
(2.73)
43.22
51.90
47.67
33.33
44.01
59.66
46.55
40.44
31.03
30.74
52.48
33.93
40.81
38.57
29.71
32.29
39.48
32.81
40.40
24.53
44.05
36.11
51.18
38.55
28.60
45.18
39.56
31.01
47.68
49.23
40.16
(1.94)
(3.98)
(2.13)
(1.83)
(2.50)
(3.70)
(2.43)
(2.46)
(1.69)
(2.02)
(2.75)
(2.50)
(2.49)
(2.73)
(2.20)
(1.83)
(3.68)
(3.15)
(1.35)
(1.49)
(2.78)
(3.29)
(2.32)
(2.35)
(1.69)
(3.18)
(3.09)
(3.43)
(2.61)
(2.81)
(2.63)
0.40
12.16
-0.10
0.24
-0.43
16.16
0.32
3.12
-0.84
-0.82
-2.46
-6.05
-6.04
-1.17
2.02
3.09
1.50
2.43
-0.95
-1.26
0.97
0.32
-1.81
-1.80
0.74
0.71
2.43
0.58
-1.15
0.02
0.74
(3.26)
(4.46)
(2.58)
(2.77)
(2.59)
(4.26)
(3.00)
(3.17)
(2.12)
(3.40)
(3.42)
(3.38)
(3.65)
(3.47)
(3.73)
(2.61)
(4.69)
(3.82)
(2.30)
(1.44)
(3.59)
(3.90)
(3.49)
(2.71)
(1.92)
(3.70)
(3.90)
(2.06)
(3.28)
(3.10)
(3.28)
Partners
Males
S.E.
Score point
difference
associated
with one
standard
deviation
change on the
ESCS
S.E.
Score point
difference
S.E.
Argentina
Azerbaijan
Bulgaria
Brazil
Chile
Colombia
Estonia
Hong Kong-China
Croatia
Indonesia
Israel
Jordan
Kyrgyztan
Liechtenstein
Lithuania
Latvia
Macao-China
Montenegro
Qatar
Romania
Russian Federation
Serbia
Slovenia
Chinese Taipei
Thailand
Tunisia
Uruguay
-0.60
-0.47
-0.24
-1.06
-0.65
-0.99
0.20
-0.74
-0.10
-1.51
0.28
-0.55
-0.65
0.15
0.06
0.02
-0.92
0.04
0.23
-0.33
-0.07
-0.14
0.14
-0.31
-1.45
-1.18
-0.47
(0.07)
(0.04)
(0.05)
(0.04)
(0.06)
(0.07)
(0.02)
(0.04)
(0.02)
(0.06)
(0.03)
(0.05)
(0.03)
(0.06)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.04)
(0.03)
(0.03)
(0.02)
(0.02)
(0.05)
(0.07)
(0.04)
-0.67
-0.42
-0.18
-1.17
-0.76
-1.01
0.08
-0.61
-0.12
-1.53
0.16
-0.59
-0.67
0.23
0.01
-0.06
-0.90
-0.08
0.20
-0.42
-0.13
-0.14
0.12
-0.31
-1.42
-1.22
-0.55
(0.07)
(0.03)
(0.05)
(0.04)
(0.07)
(0.06)
(0.02)
(0.05)
(0.03)
(0.05)
(0.03)
(0.05)
(0.02)
(0.07)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.04)
(0.03)
(0.03)
(0.02)
(0.03)
(0.04)
(0.09)
(0.03)
0.08
-0.04
-0.05
0.11
0.12
0.02
0.12
-0.12
0.01
0.02
0.12
0.03
0.01
-0.08
0.05
0.08
-0.01
0.12
0.03
0.09
0.06
-0.00
0.01
0.00
-0.02
0.04
0.09
(0.10)
(0.05)
(0.07)
(0.05)
(0.09)
(0.09)
(0.03)
(0.06)
(0.03)
(0.08)
(0.04)
(0.07)
(0.04)
(0.09)
(0.04)
(0.04)
(0.03)
(0.03)
(0.02)
(0.06)
(0.04)
(0.05)
(0.03)
(0.04)
(0.06)
(0.11)
(0.05)
35.39
10.99
51.17
28.63
37.45
22.35
32.16
26.59
32.72
21.77
43.05
28.28
28.73
50.96
36.18
28.26
14.05
21.92
17.65
34.86
32.04
33.40
45.22
39.32
28.51
15.25
35.24
(3.25)
(2.31)
(4.21)
(2.42)
(2.44)
(2.35)
(2.72)
(2.95)
(2.45)
(3.18)
(3.94)
(2.45)
(3.00)
(7.78)
(2.52)
(2.71)
(2.20)
(2.03)
(1.57)
(3.36)
(3.02)
(2.37)
(2.35)
(2.72)
(2.59)
(2.14)
(1.91)
41.29
11.81
51.62
30.06
37.09
23.89
31.17
25.87
36.08
19.69
42.32
26.20
25.69
46.82
40.51
30.22
12.62
27.42
10.34
34.71
32.39
32.14
46.57
44.46
27.26
21.87
32.89
(2.62)
(2.01)
(3.62)
(1.94)
(2.23)
(2.39)
(2.80)
(3.20)
(2.28)
(2.91)
(3.28)
(2.03)
(3.31)
(6.93)
(2.41)
(2.90)
(2.03)
(1.94)
(1.34)
(3.84)
(3.09)
(2.44)
(2.29)
(2.55)
(1.58)
(2.68)
(1.44)
5.89
0.82
0.45
1.43
-0.36
1.54
-1.00
-0.72
3.37
-2.09
-0.73
-2.08
-3.04
-4.14
4.33
1.96
-1.43
5.50
-7.31
-0.15
0.35
-1.26
1.36
5.14
-1.25
6.62
-2.35
(3.41)
(1.80)
(3.47)
(2.00)
(3.09)
(3.56)
(3.77)
(4.07)
(2.81)
(3.06)
(4.83)
(2.94)
(3.62)
(9.52)
(3.03)
(3.29)
(3.05)
(2.71)
(1.96)
(2.47)
(3.12)
(3.14)
(3.35)
(3.18)
(2.51)
(2.35)
(1.98)
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
63
Appendix B – dATA TABLES
Table 8 Gender differences in performance after accounting for the PISA index of economic. social and cultural
status of students (ESCS) (PISA 2006)
Science
64
Reading
Gender differences
(M-F)
Gender differences
(M-F) after
accounting for
the PISA index of
economic. social
and cultural status
of students
Score dif.
S.E.
Score dif.
0
8
1
4
5
9
-3
3
7
-11
6
-6
0
3
3
-2
9
7
7
-4
-4
3
5
6
4
1
6
-12
10
1
2
(3.76)
(4.91)
(4.13)
(2.19)
(5.64)
(3.24)
(2.88)
(4.03)
(3.71)
(4.68)
(4.17)
(3.44)
(4.31)
(3.53)
(7.40)
(5.55)
(2.93)
(2.19)
(3.03)
(5.22)
(3.39)
(2.48)
(3.33)
(4.73)
(2.36)
(2.97)
(2.67)
(4.12)
(3.44)
(3.51)
(0.72)
1
7
-1
6
3
7
-3
1
3
-15
5
-8
-1
1
5
-2
5
6
4
-1
-5
0
3
4
3
-1
6
-8
9
3
1
S.E.
Score dif.
S.E.
Score dif.
S.E.
Score dif.
S.E.
Score dif.
S.E.
(2.96)
(4.15)
(3.26)
(2.02)
(4.94)
(3.13)
(2.75)
(3.61)
(3.02)
(4.14)
(3.27)
(3.32)
(3.46)
(3.17)
(6.46)
(4.66)
(2.49)
(1.95)
(3.03)
(4.36)
(3.29)
(2.46)
(2.84)
(3.82)
(2.10)
(2.79)
(2.53)
(3.61)
(2.89)
(3.20)
(0.63)
-37
-45
-40
-32
-46
-30
-51
-35
-42
-57
-40
-48
-34
-41
-31
-35
-32
-34
-24
-37
-46
-40
-33
-42
-35
-40
-31
-44
-29
m
-38
(3.61)
(5.97)
(4.77)
(2.29)
(6.21)
(3.16)
(2.84)
(4.41)
(3.86)
(5.62)
(4.14)
(3.32)
(4.86)
(3.99)
(7.70)
(5.91)
(3.16)
(2.49)
(3.44)
(4.59)
(3.33)
(2.93)
(3.73)
(5.37)
(2.07)
(3.23)
(2.60)
(4.32)
(3.47)
m
(0.79)
-36
-45
-41
-30
-47
-31
-51
-36
-47
-60
-41
-50
-34
-44
-28
-36
-36
-34
-27
-35
-47
-45
-36
-44
-36
-43
-31
-40
-30
m
-39
(2.86)
(5.24)
(3.84)
(2.11)
(5.50)
(3.05)
(2.67)
(4.15)
(3.34)
(5.13)
(3.18)
(3.15)
(4.05)
(3.56)
(6.58)
(5.19)
(2.77)
(2.18)
(3.41)
(3.75)
(3.31)
(2.97)
(3.15)
(4.48)
(1.88)
(2.94)
(2.41)
(3.98)
(2.88)
m
(0.69)
14
23
7
14
11
10
12
6
20
5
10
-4
11
17
20
9
17
9
13
11
6
9
15
14
9
5
13
6
17
9
11
(3.45)
(4.70)
(4.84)
(1.91)
(5.56)
(2.77)
(2.61)
(3.68)
(3.72)
(4.48)
(4.28)
(3.22)
(4.09)
(3.36)
(7.21)
(6.33)
(2.84)
(2.57)
(2.80)
(4.71)
(3.14)
(2.57)
(3.26)
(4.61)
(2.23)
(2.93)
(2.71)
(4.57)
(2.90)
(2.89)
(0.71)
15
22
6
16
9
9
12
5
15
1
9
-7
11
14
22
8
13
9
10
13
5
5
12
12
8
2
14
10
16
11
10
(2.81)
(4.33)
(3.84)
(1.78)
(4.81)
(2.66)
(2.40)
(3.31)
(3.17)
(3.91)
(3.44)
(3.11)
(3.19)
(2.95)
(6.14)
(5.23)
(2.56)
(2.27)
(2.69)
(4.12)
(3.13)
(2.53)
(2.83)
(3.72)
(1.93)
(2.84)
(2.55)
(4.05)
(2.36)
(2.56)
(0.62)
-54
-20
-58
-32
-17
-19
-46
-31
-50
-18
-42
-55
-51
-45
-51
-50
-26
-45
-66
-44
-38
-42
-54
-21
-54
-38
-45
(7.32)
(2.60)
(6.30)
(3.00)
(5.65)
(5.31)
(2.70)
(4.51)
(4.73)
(6.32)
(6.79)
(6.53)
(3.38)
(11.74)
(3.00)
(3.23)
(2.40)
(2.85)
(2.60)
(3.40)
(3.18)
(4.01)
(2.66)
(5.44)
(4.66)
(3.62)
(4.92)
-58
-19
-56
-36
-21
-19
-49
-28
-50
-18
-45
-55
-51
-39
-53
-53
-26
-48
m
-47
-40
-42
-54
-21
-54
-38
-48
(7.09)
(2.53)
(5.44)
(2.93)
(4.72)
(5.02)
(2.65)
(4.14)
(4.39)
(5.81)
(6.37)
(5.24)
(3.11)
(10.44)
(3.02)
(3.11)
(2.41)
(2.75)
m
(2.85)
(2.96)
(3.38)
(2.34)
(4.55)
(3.79)
(3.74)
(4.70)
13
-1
-4
19
28
22
1
16
13
17
12
-7
1
0
2
5
11
12
-14
7
6
5
5
13
-7
15
13
(5.64)
(2.03)
(4.91)
(2.76)
(4.75)
(4.56)
(3.19)
(5.48)
(3.78)
(7.27)
(6.94)
(6.49)
(2.86)
(11.65)
(2.97)
(2.98)
(2.87)
(3.27)
(2.10)
(3.32)
(3.35)
(4.47)
(2.94)
(6.65)
(4.23)
(3.62)
(4.23)
9
-1
-2
15
24
21
-2
19
13
17
9
-7
1
4
0
3
11
9
m
3
3
5
5
14
-6
15
10
(5.27)
(2.02)
(4.19)
(2.68)
(3.75)
(3.86)
(3.03)
(4.77)
(3.40)
(6.78)
(6.38)
(5.32)
(2.49)
(10.31)
(2.97)
(2.90)
(2.88)
(3.27)
m
(3.03)
(3.03)
(3.84)
(2.54)
(5.53)
(3.29)
(3.45)
(3.87)
OECD
Gender differences
(M-F)
Gender differences
(M-F) after
accounting for
the PISA index of
economic. social
and cultural status
of students
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
OECD average
Partners
Gender differences
(M-F)
Gender differences
(M-F) after
accounting for
the PISA index of
economic. social
and cultural status
of students
Mathematics
Argentina
-13
(5.62)
-17
(5.25)
Azerbaijan
-8
(2.04)
-7
(1.96)
Bulgaria
-17
(5.80)
-15
(4.91)
Brazil
9
(2.33)
5
(2.17)
Chile
22
(4.82)
18
(3.81)
Colombia
9
(4.62)
8
(3.95)
Estonia
-4
(3.10)
-7
(2.95)
Hong Kong-China
7
(4.85)
10
(4.23)
Croatia
-2
(4.07)
-3
(3.71)
Indonesia
12
(6.35)
11
(5.88)
Israel
3
(6.55)
0
(5.86)
Jordan
-29
(5.31)
-29
(4.13)
Kyrgyztan
-6
(2.95)
-6
(2.69)
Liechtenstein
-11
(11.14)
-7
(9.85)
Lithuania
-9
(2.78)
-11
(2.80)
Latvia
-7
(3.09)
-9
(2.89)
Macao-China
4
(2.74)
4
(2.73)
Montenegro
-2
(2.59)
-4
(2.56)
Qatar
-32
(1.90)
m
m
Romania
-2
(3.25)
-5
(2.66)
Russian Federation
3
(2.65)
1
(2.54)
Serbia
-5
(3.81)
-5
(3.30)
Slovenia
-8
(3.23)
-8
(2.80)
Chinese Taipei
7
(5.95)
8
(4.97)
Thailand
-17
(3.86)
-16
(3.04)
Tunisia
-5
(3.38)
-5
(3.32)
Uruguay
-3
(3.95)
-5
(3.75)
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 9 Variation of within-school gender differences according to the socio-economic background of student and
school (ESCS) (PISA 2006)
Partners
OECD
Intercept
Female
Student ESCS
Interaction between
female and school
average ESCS
S.E.
Change
in score
S.E.
Change in
score
S.E.
0.1
(1.53)
29.0
(1.43)
64.3
(3.14)
-15.6
(4.91)
(1.45)
-15.8
(2.51)
9.7
(1.64)
106.2
(4.12)
12.2
(5.39)
(0.92)
-11.7
(1.54)
16.8
(1.22)
108.6
(2.30)
-9.7
(3.13)
535.6
(0.89)
-7.5
(1.18)
23.1
(1.46)
46.1
(2.53)
-4.3
(2.65)
Czech Republic
523.4
(1.26)
-15.3
(2.31)
17.5
(1.66)
119.2
(3.77)
6.0
(5.17)
Denmark
500.9
(1.82)
-8.1
(2.50)
32.3
(1.88)
40.9
(6.28)
1.4
(6.50)
Finland
561.9
(1.84)
3.0
(2.61)
29.7
(1.64)
14.1
(6.39)
-7.0
(10.27)
France
504.8
(1.13)
-16.9
(1.85)
18.2
(1.87)
106.8
(2.59)
-9.4
(4.16)
Germany
521.9
(1.39)
-15.3
(2.28)
13.5
(1.42)
115.9
(3.52)
-0.7
(4.12)
Greece
470.5
(1.52)
-3.7
(2.55)
15.5
(1.51)
68.4
(2.96)
-4.8
(4.36)
Hungary
514.7
(1.20)
-25.2
(2.32)
4.6
(1.34)
92.4
(3.22)
-6.6
(4.04)
Iceland
493.5
(2.97)
7.2
(3.22)
29.3
(1.92)
-2.3
(7.47)
-4.3
(10.13)
Ireland
509.5
(1.98)
-1.2
(3.00)
27.9
(1.98)
55.3
(5.42)
-15.1
(8.45)
Italy
486.8
(0.84)
-10.7
(1.17)
6.4
(0.94)
93.5
(1.54)
-8.7
(2.17)
Japan
533.4
(1.14)
-3.0
(2.01)
5.1
(2.27)
138.1
(4.59)
-8.8
(7.39)
Korea
520.4
(1.70)
0.1
(2.95)
8.9
(1.86)
83.7
(3.80)
-8.5
(5.95)
Luxembourg
488.7
(1.81)
-8.2
(2.62)
23.8
(1.36)
73.3
(3.14)
-9.5
(5.00)
Mexico
421.0
(0.45)
-14.9
(0.76)
5.6
(0.76)
38.4
(1.17)
-0.9
(1.01)
Netherlands
532.0
(1.16)
-14.3
(1.71)
10.0
(1.31)
121.5
(2.46)
6.2
(3.38)
New Zealand
533.8
(2.20)
-2.3
(3.46)
40.6
(1.88)
56.3
(6.11)
-2.4
(8.61)
Norway
488.1
(1.88)
4.7
(2.87)
30.6
(1.94)
30.8
(7.48)
-2.8
(9.45)
Poland
499.5
(1.50)
-1.1
(2.31)
35.3
(1.57)
26.2
(4.50)
-9.8
(5.05)
Portugal
478.1
(1.52)
-7.8
(2.14)
16.7
(1.23)
34.1
(2.13)
-3.4
(2.51)
Slovak Republic
494.5
(1.29)
-13.7
(2.44)
19.7
(1.84)
60.1
(4.27)
-7.3
(3.68)
Spain
496.5
(0.91)
-4.4
(1.19)
24.1
(1.33)
25.7
(1.86)
-8.7
(1.87)
Sweden
505.5
(1.67)
0.7
(2.44)
32.3
(2.89)
42.6
(9.47)
-17.3
(10.05)
Switzerland
511.7
(0.90)
-13.7
(1.63)
25.9
(1.46)
69.5
(2.99)
2.1
(3.35)
Turkey
424.1
(0.96)
1.3
(1.98)
8.5
(1.20)
64.9
(2.24)
0.5
(3.21)
United Kingdom
520.0
(1.15)
-9.0
(1.63)
31.9
(1.99)
71.6
(3.95)
-1.7
(5.26)
United States
492.0
(1.38)
-3.8
(2.47)
33.9
(1.91)
52.2
(3.58)
-1.8
(4.93)
OECD average
504.1
(0.27)
-7.0
(0.41)
20.9
(0.31)
67.3
(0.81)
-4.7
(1.06)
Argentina
389.3
(1.77)
1.2
(2.70)
13.6
(1.73)
54.1
(3.08)
4.1
(3.72)
Azerbaijan
380.0
(0.85)
7.8
(1.35)
6.7
(0.87)
15.9
(1.34)
-0.8
(1.84)
Brazil
387.4
(1.17)
-10.1
(1.67)
7.7
(1.26)
48.5
(1.69)
-1.5
(1.94)
Bulgaria
435.7
(1.62)
-1.9
(2.99)
12.7
(1.72)
70.4
(3.99)
-4.8
(4.65)
Chile
440.9
(1.55)
-17.0
(2.42)
9.9
(1.39)
53.7
(2.18)
2.5
(2.73)
Colombia
395.3
(1.55)
-13.2
(2.36)
10.8
(1.78)
32.9
(3.29)
-2.1
(3.38)
Croatia
499.3
(1.29)
-13.4
(2.04)
13.5
(1.40)
93.0
(3.26)
-12.2
(4.87)
Estonia
529.8
(1.38)
2.7
(1.93)
22.2
(1.77)
44.2
(5.11)
-6.2
(6.66)
Hong Kong-China
554.5
(1.53)
-19.2
(2.44)
9.0
(1.76)
63.9
(5.81)
3.3
(7.77)
Indonesia
386.8
(0.80)
-8.6
(1.13)
0.6
(1.09)
42.1
(1.59)
0.7
(1.78)
Israel
457.1
(2.07)
-2.0
(3.45)
25.9
(2.33)
79.5
(4.91)
-20.8
(7.51)
Jordan
409.3
(1.26)
26.2
(1.80)
18.2
(1.28)
40.4
(3.30)
-23.2
(4.65)
Kyrgyztan
316.9
(1.15)
6.0
(2.06)
5.5
(1.39)
80.8
(3.01)
-10.3
(5.25)
Latvia
487.0
(1.69)
5.6
(2.33)
20.9
(1.99)
37.3
(3.93)
-5.9
(5.85)
Liechtenstein
519.4
(7.86)
-11.4
(8.06)
16.8
(5.00)
131.4
(18.78)
2.6
(22.08)
Lithuania
485.3
(1.34)
4.7
(2.25)
24.1
(1.46)
49.0
(3.36)
-5.0
(5.15)
Macao-China
507.6
(1.97)
-15.3
(2.82)
6.5
(1.57)
22.4
(5.51)
-11.9
(5.78)
Montenegro
413.0
(1.73)
-8.1
(2.19)
10.0
(1.46)
62.4
(6.32)
7.0
(6.78)
m
m
m
m
Romania
421.8
(0.95)
Russian Federation
483.2
Serbia
441.5
Slovenia
Intercept
S.E.
Australia
528.2
(0.91)
Austria
514.9
Belgium
517.6
Canada
Qatar
Change
in score
School average ESCS
m
m
m
m
m
Change in
score
S.E.
m
-12.5
(1.91)
11.0
(2.68)
66.6
(3.21)
-9.7
(3.10)
(1.21)
-6.4
(2.21)
20.0
(1.78)
40.8
(5.09)
-1.5
(6.43)
(1.21)
-12.8
(2.08)
11.0
(1.24)
77.6
(2.57)
-0.2
(3.75)
515.3
(1.39)
-18.8
(2.26)
5.6
(1.43)
128.8
(2.99)
-10.9
(4.32)
Chinese Taipei
537.6
(0.87)
-8.5
(1.51)
13.4
(1.62)
104.5
(2.35)
4.2
(3.52)
Thailand
415.0
(1.36)
9.1
(2.15)
8.4
(1.26)
44.5
(2.33)
-6.1
(2.67)
Tunisia
386.1
(1.08)
-7.3
(2.16)
3.9
(1.10)
37.2
(1.84)
-2.2
(2.55)
Uruguay
434.8
(1.58)
-6.6
(2.39)
14.0
(1.41)
47.4
(2.26)
-3.7
(2.68)
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
65
Appendix B – dATA TABLES
Table 10 Performance difference between native students and immigrant students (PISA 2006)
Males
Partners
OECD
Native
Females
Difference
(Native - Immigrant)
Immigrant
S.E.
Dif.
Immigrant
Mean
S.E.
Mean
Mean
S.E.
Mean
Dif.
S.E.
529
(2.59)
531
(8.45)
-2
(8.07)
530
(2.78)
523
(4.40)
6
(4.39)
Austria
526
(4.13)
442
(7.24)
84
(7.38)
521
(4.38)
426
(16.56)
95
(17.50)
Belgium
524
(3.14)
437
(8.81)
87
(8.84)
522
(2.97)
437
(6.62)
85
(6.76)
Canada
543
(2.29)
527
(5.26)
16
(5.42)
539
(1.95)
520
(5.05)
18
(5.08)
Czech Republic
517
(4.28)
464
(15.20)
53
(15.58)
512
(4.76)
441
(25.37)
71
(25.34)
Denmark
508
(3.43)
425
(9.40)
83
(9.02)
498
(3.25)
407
(9.72)
92
(9.80)
Finland
565
(2.58)
472
(17.81)
92
(17.92)
567
(2.35)
471
(17.76)
95
(17.61)
France
506
(4.62)
452
(8.75)
55
(9.65)
503
(3.65)
451
(10.37)
51
(11.07)
Germany
535
(4.15)
450
(10.50)
85
(10.17)
529
(3.34)
443
(6.17)
86
(5.79)
Greece
472
(4.47)
427
(10.29)
45
(10.11)
483
(3.46)
439
(11.91)
44
(12.18)
Hungary
508
(3.34)
494
(18.13)
15
(19.06)
501
(3.44)
509
(16.82)
-8
(15.92)
Iceland
493
(2.58)
413
(18.98)
80
(18.71)
496
(2.19)
426
(19.42)
70
(19.67)
Ireland
510
(4.18)
494
(12.59)
16
(11.84)
511
(3.13)
505
(16.19)
6
(15.96)
Italy
482
(2.64)
417
(12.99)
65
(12.60)
477
(2.56)
425
(7.22)
52
(7.50)
Japan
534
(4.85)
503
(35.37)
30
(34.83)
530
(5.11)
470
(67.91)
60
(66.97)
Korea
523
(4.76)
c
c
524
(3.81)
593
(28.21)
-69
(28.55)
Luxembourg
516
(2.29)
447
(3.55)
69
(4.66)
506
(2.35)
443
(3.17)
64
(4.16)
Mexico
418
(3.12)
317
(14.70)
101
(14.25)
413
(2.44)
320
(8.83)
92
(8.44)
Netherlands
539
(2.99)
460
(9.91)
79
(9.88)
530
(2.67)
458
(11.59)
72
(11.93)
New Zealand
535
(4.08)
521
(7.62)
14
(8.09)
537
(3.56)
519
(6.83)
18
(6.68)
Norway
493
(3.13)
426
(11.77)
66
(11.07)
493
(2.88)
443
(10.98)
50
(10.65)
Poland
501
(2.67)
433
(79.11)
68
(78.67)
497
(2.62)
535
(34.69)
-38
(34.55)
Portugal
480
(3.68)
434
(13.08)
46
(12.68)
477
(2.94)
415
(11.77)
61
(11.96)
Slovak Republic
493
(3.89)
427
(32.81)
66
(32.65)
486
(3.04)
466
(26.89)
21
(26.68)
Spain
497
(2.71)
430
(10.10)
67
(9.92)
491
(2.59)
438
(7.81)
53
(7.62)
Sweden
512
(2.51)
459
(8.40)
53
(7.81)
512
(3.15)
444
(6.24)
68
(6.95)
Switzerland
534
(2.89)
450
(6.04)
84
(5.50)
527
(3.59)
449
(5.18)
79
(4.90)
Turkey
419
(4.60)
427
(20.27)
-8
(20.16)
432
(4.11)
462
(18.12)
-30
(17.44)
United Kingdom
526
(2.51)
485
(12.68)
40
(12.46)
514
(2.61)
489
(10.56)
25
(10.46)
United States
500
(5.28)
453
(7.57)
46
(7.79)
498
(4.03)
447
(7.34)
51
(7.66)
OECD average
508
(0.65)
452
(3.82)
55
(3.80)
505
(0.60)
460
(3.52)
45
(3.50)
Argentina
386
(6.71)
378
(17.86)
8
(17.61)
399
(6.97)
379
(9.24)
20
(10.52)
Azerbaijan
381
(3.25)
377
(8.00)
4
(8.82)
387
(2.86)
387
(8.23)
0
(8.35)
Brazil
398
(3.26)
348
(11.90)
50
(12.18)
388
(2.88)
349
(10.91)
40
(10.80)
Bulgaria
428
(6.54)
365
(47.49)
62
(47.01)
445
(6.87)
362
(29.15)
83
(29.80)
Chile
450
(5.55)
382
(39.31)
67
(39.15)
428
(4.46)
412
(23.93)
16
(23.96)
Colombia
396
(4.15)
344
(22.25)
52
(22.64)
386
(4.03)
269
(29.68)
117
(29.99)
Croatia
495
(3.46)
478
(6.10)
17
(6.55)
498
(3.14)
476
(5.64)
22
(5.37)
Estonia
536
(3.08)
501
(6.63)
35
(6.85)
538
(2.89)
507
(6.07)
30
(6.13)
Hong Kong-China
552
(4.38)
541
(4.98)
11
(6.76)
542
(4.35)
534
(3.89)
8
(4.69)
Indonesia
400
(8.06)
295
(7.72)
105
(11.41)
388
(3.74)
297
(10.54)
91
(10.93)
Israel
467
(5.50)
459
(7.82)
7
(6.68)
457
(4.42)
453
(7.09)
4
(6.69)
Jordan
408
(4.25)
435
(7.33)
-27
(7.15)
434
(3.52)
459
(4.61)
-25
(5.03)
Kyrgyztan
322
(3.32)
368
(18.76)
-46
(17.91)
326
(2.96)
378
(14.55)
-52
(14.08)
Latvia
489
(3.53)
485
(8.05)
4
(8.27)
495
(3.29)
493
(6.66)
2
(6.95)
Liechtenstein
531
(9.64)
492
(14.83)
39
(18.42)
547
(7.76)
493
(13.84)
54
(17.33)
Lithuania
485
(3.17)
485
(14.50)
1
(15.10)
493
(3.17)
490
(15.73)
3
(16.13)
Macao-China
502
(3.68)
518
(2.16)
-16
(4.30)
505
(2.72)
512
(1.92)
-6
(3.35)
Montenegro
411
(2.01)
422
(9.03)
-11
(9.75)
412
(1.74)
436
(8.24)
-24
(8.48)
Qatar
309
(1.69)
377
(2.32)
-68
(3.11)
347
(1.49)
399
(2.76)
-52
(3.30)
Romania
417
(4.13)
551
(23.25)
-134
(23.78)
419
(4.81)
547
(38.86)
-127
(39.00)
Russian Federation
483
(4.33)
471
(6.50)
11
(7.72)
480
(3.85)
464
(8.13)
16
(8.01)
Serbia
433
(3.47)
439
(5.62)
-6
(6.04)
438
(3.72)
449
(7.19)
-11
(6.73)
Slovenia
522
(2.07)
468
(6.13)
54
(6.43)
529
(2.25)
471
(7.67)
58
(8.79)
Chinese Taipei
539
(4.09)
487
(19.77)
51
(19.88)
530
(5.03)
482
(22.19)
49
(20.93)
Thailand
413
(3.31)
309
(31.06)
104
(30.78)
429
(2.49)
375
(68.42)
54
(68.57)
Tunisia
385
(3.21)
304
(20.66)
80
(21.18)
389
(3.54)
321
(14.85)
68
(14.63)
Uruguay
429
(3.99)
445
(59.21)
-15
(58.82)
431
(2.70)
438
(29.78)
-7
(29.28)
c
S.E.
Difference
(Native - Immigrant)
Australia
c
S.E.
Native
Source: OECD PISA 2006 Database.
66
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 11 Percentage of students whose parents report various science activities at age 10 very often and regularly
Watching science TV
programmes
Males
%
Reading science books
Females
S.E.
%
S.E.
Males
%
Females
S.E.
%
S.E.
Visiting science
websites
Reading science fiction
Males
%
S.E.
Females
%
S.E.
Males
%
S.E.
Belonging to a science
club
Females
%
Males
Females
S.E.
%
S.E.
%
Bulgaria
38.5 (1.3) 26.2 (1.3) 11.3 (1.0) 11.4 (0.7) 36.3 (1.5) 24.1 (0.9) 19.9 (1.1) 14.4 (0.9)
m
m
S.E.
Colombia
42.5 (1.4) 32.2 (1.8) 26.9 (1.5) 23.3 (1.2) 59.9 (1.3) 48.4 (1.4) 15.7 (1.0) 11.8 (1.0)
6.3 (0.7)
Croatia
35.6 (1.0) 25.4 (1.0) 13.0 (0.7)
9.6 (0.7) 38.3 (1.0) 24.1 (0.8)
5.7 (0.6)
3.7 (0.4)
2.6 (0.3)
3.4 (0.4)
Denmark
35.2 (1.3) 21.9 (1.0) 14.3 (1.0)
5.8 (0.6) 36.1 (1.4) 13.8 (1.0)
6.0 (0.8)
2.6 (0.4)
5.1 (0.6)
6.9 (0.6)
Germany
31.3 (1.1) 16.7 (1.0) 16.0 (1.0)
9.4 (0.8) 18.3 (0.9)
6.9 (0.6)
3.9 (0.4)
2.2 (0.3)
3.3 (0.5)
2.8 (0.5)
Hong Kong-China
16.3 (1.0) 10.4 (0.6) 11.3 (0.8)
7.3 (0.6) 10.5 (0.7) 10.0 (0.6)
6.4 (0.5)
3.8 (0.4)
5.1 (0.5)
4.5 (0.4)
0.8 (0.3)
m
m
6.3 (0.6)
Iceland
27.4 (1.4) 10.4 (0.8) 16.3 (1.1)
5.7 (0.6) 12.1 (1.1)
4.8 (0.6)
5.8 (0.8)
2.3 (0.4)
1.8 (0.3)
Italy
38.9 (0.9) 22.6 (0.7) 15.8 (0.6)
9.4 (0.6) 47.4 (1.0) 38.5 (0.8)
7.6 (0.5)
4.0 (0.4)
3.8 (0.4)
2.8 (0.3)
Korea
15.2 (0.7)
8.8 (0.6) 20.7 (1.1) 14.8 (0.9) 18.7 (1.0) 13.5 (0.7)
5.0 (0.4)
2.9 (0.3)
6.6 (0.5)
4.2 (0.4)
Luxembourg
31.4 (1.2) 19.4 (1.0) 20.4 (0.9) 13.1 (0.7) 21.7 (1.0)
Macao-China
16.6 (0.9) 11.6 (0.7)
8.5 (0.6)
9.7 (0.7)
4.7 (0.5)
3.8 (0.4)
6.4 (0.6)
4.5 (0.5)
6.2 (0.5) 12.9 (0.7) 13.7 (0.8)
6.8 (0.6)
4.7 (0.6)
3.2 (0.4)
2.0 (0.3)
1.3 (0.3)
New Zealand
20.1 (1.0) 13.8 (0.8) 17.0 (1.0)
8.7 (0.6) 26.4 (1.3) 16.4 (0.9)
6.0 (0.5)
5.7 (0.5)
1.4 (0.3)
Portugal
27.7 (1.2) 19.3 (0.8) 11.9 (0.8)
9.8 (0.7) 24.2 (1.3) 17.1 (0.8)
8.3 (0.8)
5.9 (0.5)
2.4 (0.4)
3.4 (0.5)
Qatar
32.3 (1.0) 24.5 (1.0) 17.5 (0.9) 13.3 (0.7) 35.3 (1.1) 27.4 (0.9) 15.8 (0.9) 11.3 (0.7) 15.0 (0.9)
7.3 (0.5)
Turkey
16.7 (1.0) 13.8 (0.8) 16.5 (1.0) 15.5 (0.7) 42.5 (1.3) 31.0 (1.3) 13.6 (0.8)
3.6 (0.6)
4.6 (0.5)
4.8 (0.6)
Source: OECD PISA 2006 Database.
Table 12 Percentage of students whose parents agree with statements regarding the school of their son or daughter
Teachers competent
Males
Discipline good
Females
Males
Achievements high
Females
Males
Females
%
S.E.
%
S.E.
%
S.E.
%
S.E.
%
S.E.
%
S.E.
Bulgaria
95.5
(0.7)
95.3
(0.5)
82.5
(1.1)
78.1
(1.2)
86.3
(1.1)
88.1
(0.9)
Colombia
95.6
(0.6)
93.3
(0.8)
83.0
(1.2)
82.5
(1.4)
85.7
(1.2)
86.6
(1.6)
Croatia
93.2
(0.5)
91.3
(0.6)
80.9
(1.0)
83.5
(1.0)
62.5
(1.5)
68.8
(1.2)
Denmark
89.0
(0.8)
86.8
(1.1)
74.5
(1.3)
74.1
(1.8)
76.7
(1.5)
77.9
(1.7)
Germany
79.7
(0.9)
79.8
(0.8)
74.4
(1.1)
73.2
(1.3)
69.9
(1.3)
72.8
(1.3)
Hong Kong-China
90.1
(0.7)
89.4
(0.7)
87.1
(0.9)
90.0
(0.7)
51.9
(1.7)
55.6
(1.7)
Iceland
85.5
(1.0)
86.4
(0.8)
78.6
(1.1)
74.0
(1.1)
69.5
(1.3)
74.9
(1.2)
Italy
91.8
(0.4)
90.7
(0.6)
79.9
(0.8)
81.8
(0.6)
78.6
(0.8)
81.5
(0.6)
Korea
86.3
(0.7)
80.2
(1.1)
80.1
(0.9)
76.6
(1.3)
72.3
(1.2)
70.6
(1.5)
Luxembourg
84.6
(1.0)
84.4
(0.8)
83.2
(1.0)
82.5
(1.0)
74.2
(1.1)
79.0
(1.0)
Macao-China
90.2
(0.7)
87.7
(0.8)
84.5
(0.9)
82.9
(0.9)
74.8
(0.9)
73.0
(1.0)
New Zealand
93.6
(0.6)
93.3
(0.5)
82.7
(1.1)
82.7
(1.1)
85.1
(1.1)
88.8
(0.9)
Portugal
94.1
(0.6)
93.6
(0.6)
81.9
(1.2)
79.0
(1.2)
77.4
(1.2)
75.0
(1.1)
Qatar
85.1
(0.9)
88.2
(0.7)
79.2
(1.0)
79.5
(0.9)
78.0
(1.0)
82.3
(0.8)
Turkey
86.2
(0.8)
87.4
(0.9)
83.0
(0.8)
80.5
(1.1)
70.4
(1.3)
75.8
(1.3)
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
67
Appendix B – dATA TABLES
Table 13 [Part 1/2] Index of students’ perceptions of school preparation for science-related careers
Results based on students’ self-reports
Index of students’ perceptions of school preparation for science-related careers
Females
Gender
difference
(M - F)
Bottom
quarter
Second
quarter
Third
quarter
Top
quarter
Mean
index
S.E.
Mean
index
S.E.
Mean
index
S.E.
Dif.
S.E.
Mean
index
S.E.
Mean
index
S.E.
Mean
index
S.E.
OECD
Males
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
OECD average
0.21
-0.24
-0.13
0.33
-0.18
-0.04
0.17
0.07
0.10
-0.12
0.04
0.05
0.18
-0.09
-0.52
-0.27
-0.12
0.48
-0.24
0.20
-0.31
0.03
0.22
-0.14
0.07
-0.07
0.02
-0.14
0.21
0.25
0.00
(0.02)
(0.03)
(0.02)
(0.01)
(0.03)
(0.02)
(0.01)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.03)
(0.01)
(0.02)
(0.02)
(0.03)
(0.01)
(0.02)
(0.00)
0.19
-0.22
-0.15
0.29
-0.24
-0.04
0.14
0.04
0.11
-0.04
0.03
0.06
0.09
-0.01
-0.44
-0.21
-0.14
0.50
-0.19
0.16
-0.26
-0.02
0.22
-0.17
0.03
-0.05
0.01
-0.10
0.23
0.25
0.00
(0.03)
(0.04)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.04)
(0.03)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.01)
0.23
-0.26
-0.11
0.36
-0.10
-0.05
0.20
0.10
0.09
-0.21
0.06
0.04
0.27
-0.17
-0.60
-0.34
-0.09
0.46
-0.29
0.24
-0.37
0.08
0.21
-0.11
0.11
-0.09
0.03
-0.19
0.19
0.24
-0.00
(0.02)
(0.04)
(0.02)
(0.02)
(0.04)
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.04)
(0.02)
(0.03)
(0.03)
(0.03)
(0.02)
(0.03)
(0.01)
-0.04
0.04
-0.04
-0.07
-0.14
0.01
-0.06
-0.06
0.02
0.18
-0.03
0.02
-0.18
0.16
0.15
0.14
-0.06
0.04
0.10
-0.08
0.11
-0.10
0.01
-0.06
-0.08
0.04
-0.03
0.09
0.04
0.00
0.00
(0.03)
(0.05)
(0.03)
(0.02)
(0.05)
(0.03)
(0.03)
(0.04)
(0.04)
(0.03)
(0.03)
(0.03)
(0.04)
(0.02)
(0.04)
(0.03)
(0.03)
(0.03)
(0.03)
(0.04)
(0.03)
(0.03)
(0.03)
(0.04)
(0.03)
(0.03)
(0.02)
(0.04)
(0.03)
(0.03)
(0.01)
-0.97
-1.67
-1.32
-0.86
-1.29
-1.15
-0.86
-1.23
-1.30
-1.27
-1.10
-1.18
-1.05
-1.29
-1.66
-1.37
-1.48
-0.67
-1.21
-0.93
-1.50
-1.06
-0.89
-1.27
-1.13
-1.26
-1.28
-1.58
-0.89
-0.87
-1.19
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
(0.00)
-0.09
-0.73
-0.50
0.04
-0.46
-0.24
0.04
-0.24
-0.25
-0.50
-0.20
-0.18
-0.11
-0.44
-0.93
-0.59
-0.52
0.15
-0.60
-0.06
-0.63
-0.17
-0.01
-0.44
-0.18
-0.28
-0.34
-0.50
-0.04
0.01
-0.30
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
0.34
0.15
0.11
0.48
0.05
0.07
0.14
0.30
0.47
0.16
0.24
0.19
0.40
0.19
-0.19
0.04
0.22
0.80
0.01
0.31
-0.01
0.14
0.32
0.06
0.26
0.06
0.28
0.17
0.25
0.31
0.21
(0.01)
(0.01)
(0.00)
(0.01)
(0.00)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
1.54
1.30
1.21
1.64
0.99
1.15
1.35
1.45
1.49
1.11
1.23
1.39
1.50
1.17
0.71
0.83
1.33
1.64
0.83
1.49
0.90
1.21
1.44
1.09
1.32
1.20
1.41
1.36
1.51
1.54
1.28
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.02)
(0.03)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
(0.01)
(0.01)
(0.00)
Partners
All students
Mean
index
Argentina
Azerbaijan
Brazil
Bulgaria
Chile
Colombia
Croatia
Estonia
Hong Kong-China
Indonesia
Israel
Jordan
Kyrgyzstan
Latvia
Liechtenstein
Lithuania
Macao-China
Montenegro
Qatar
Romania
Russian Federation
Serbia
Slovenia
Chinese Taipei
Thailand
Tunisia
Uruguay
0.12
0.65
0.16
0.40
0.22
0.51
0.18
0.28
-0.13
0.33
-0.08
0.50
0.64
0.22
0.12
0.43
-0.17
0.34
0.17
0.40
0.30
0.15
0.07
0.25
0.63
0.55
0.09
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.01)
(0.05)
(0.02)
(0.01)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
0.15
0.63
0.20
0.34
0.28
0.54
0.18
0.26
-0.04
0.29
-0.12
0.44
0.58
0.20
0.07
0.38
-0.06
0.31
0.23
0.33
0.30
0.16
0.02
0.31
0.55
0.53
0.11
(0.03)
(0.03)
(0.03)
(0.02)
(0.03)
(0.04)
(0.03)
(0.02)
(0.02)
(0.04)
(0.04)
(0.03)
(0.02)
(0.02)
(0.09)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
0.10
0.68
0.13
0.45
0.15
0.49
0.19
0.30
-0.23
0.37
-0.04
0.56
0.70
0.23
0.15
0.47
-0.28
0.37
0.12
0.47
0.29
0.14
0.13
0.20
0.70
0.56
0.06
(0.03)
(0.03)
(0.02)
(0.02)
(0.04)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.08)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
0.04
-0.04
0.06
-0.11
0.13
0.05
-0.01
-0.05
0.19
-0.07
-0.08
-0.12
-0.12
-0.03
-0.08
-0.09
0.23
-0.06
0.10
-0.14
0.01
0.02
-0.11
0.11
-0.15
-0.03
0.05
(0.03)
(0.04)
(0.03)
(0.03)
(0.04)
(0.04)
(0.03)
(0.03)
(0.03)
(0.03)
(0.05)
(0.04)
(0.03)
(0.03)
(0.14)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.02)
(0.03)
(0.03)
-1.14
-0.47
-1.02
-0.78
-1.12
-0.69
-0.99
-0.80
-1.22
-0.61
-1.52
-0.76
-0.49
-0.76
-1.33
-0.58
-1.23
-0.93
-1.37
-0.73
-0.78
-1.06
-1.00
-0.83
-0.20
-0.76
-0.99
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.03)
(0.03)
(0.02)
(0.02)
(0.01)
(0.06)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
-0.15
0.31
-0.11
0.05
-0.13
0.18
-0.04
0.01
-0.37
0.05
-0.49
0.24
0.32
-0.01
-0.26
0.05
-0.42
0.04
-0.13
0.05
0.04
-0.09
-0.10
0.05
0.18
0.31
-0.11
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.04)
(0.00)
(0.01)
(0.00)
(0.01)
(0.00)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
0.36
0.98
0.39
0.64
0.56
0.82
0.32
0.45
0.05
0.50
0.22
0.87
1.02
0.30
0.43
0.62
0.05
0.68
0.57
0.69
0.45
0.33
0.16
0.28
0.83
0.97
0.22
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.04)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
1.43
1.79
1.40
1.68
1.57
1.74
1.44
1.46
1.01
1.39
1.46
1.65
1.72
1.34
1.65
1.62
0.94
1.56
1.63
1.60
1.48
1.44
1.23
1.51
1.72
1.68
1.23
(0.02)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.04)
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
S.E.
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
68
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 13 [Part 2/2] Index of students’ perceptions of school preparation for science-related careers
Results based on students’ self-reports
Performance on the science scale. by national quarters
of this index
Third
quarter
Top
quarter
Explained variance in
student performance
(r-squared x 100)
Mean
score
S.E.
Mean
score
S.E.
Mean
score
S.E.
Mean
score
S.E.
Effect
S.E.
Ratio
S.E.
Percentage
S.E.
OECD
Second
quarter
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
OECD average
489
502
489
508
512
473
549
461
507
482
506
461
481
478
510
522
489
415
508
492
474
508
470
492
475
489
484
431
477
462
486
(2.5)
(4.2)
(3.5)
(2.6)
(5.1)
(4.1)
(3.5)
(4.0)
(5.7)
(4.5)
(4.1)
(3.3)
(4.3)
(3.1)
(5.2)
(4.1)
(2.7)
(3.2)
(4.0)
(4.3)
(4.3)
(3.8)
(3.4)
(4.4)
(2.7)
(4.0)
(3.7)
(4.6)
(3.1)
(4.6)
(0.7)
517
509
515
530
514
493
565
493
524
477
502
491
508
474
532
525
488
410
521
525
489
500
466
487
486
498
505
420
509
486
499
(2.94)
(4.40)
(3.38)
(3.21)
(4.32)
(3.87)
(4.00)
(4.49)
(4.37)
(4.31)
(3.65)
(3.20)
(4.96)
(2.67)
(3.78)
(3.71)
(3.88)
(3.16)
(3.68)
(4.94)
(3.34)
(3.51)
(4.19)
(4.37)
(3.61)
(3.87)
(3.58)
(5.05)
(3.89)
(4.66)
(0.72)
538
518
523
542
529
505
563
507
526
471
504
501
517
472
536
520
487
407
536
541
496
495
472
492
493
510
525
427
522
493
506
(3.71)
(5.11)
(3.05)
(3.33)
(4.65)
(4.14)
(4.18)
(4.15)
(4.60)
(4.08)
(3.74)
(3.37)
(5.60)
(2.67)
(3.87)
(3.93)
(3.07)
(2.37)
(4.05)
(3.95)
(3.90)
(3.35)
(3.99)
(4.30)
(3.37)
(3.43)
(3.49)
(5.69)
(3.20)
(5.34)
(0.73)
577
520
534
566
520
520
577
534
537
467
506
520
534
479
545
523
487
412
554
574
505
491
493
490
506
526
537
420
559
521
518
(3.8)
(6.4)
(3.8)
(2.9)
(5.0)
(4.5)
(3.2)
(4.5)
(5.0)
(4.2)
(4.9)
(3.3)
(4.2)
(3.5)
(5.2)
(5.4)
(2.7)
(4.1)
(5.0)
(3.7)
(3.8)
(3.1)
(4.4)
(4.9)
(3.5)
(3.5)
(5.1)
(5.4)
(3.1)
(6.1)
(0.8)
35.1
7.5
17.8
23.5
6.2
19.1
14.3
28.1
10.8
-5.5
0.4
23.0
20.6
1.2
14.5
1.2
0.4
-1.3
22.9
33.6
13.6
-6.9
10.5
-0.7
13.4
16.5
20.9
-0.9
34.8
25.5
13.3
(1.17)
(2.03)
(1.85)
(1.30)
(2.51)
(2.14)
(1.82)
(1.78)
(1.96)
(1.74)
(2.28)
(1.62)
(1.47)
(1.77)
(2.10)
(2.71)
(1.28)
(1.34)
(2.09)
(1.84)
(1.89)
(1.85)
(1.66)
(2.71)
(1.26)
(1.77)
(1.90)
(1.71)
(1.54)
(1.98)
(0.34)
1.9
1.1
1.5
1.6
0.9
1.5
1.3
1.7
1.3
0.8
0.9
1.8
1.6
0.8
1.4
1.0
0.9
0.9
1.4
1.8
1.3
0.8
1.0
0.9
1.2
1.4
1.4
0.7
1.8
1.5
1.3
(0.08)
(0.09)
(0.07)
(0.08)
(0.10)
(0.10)
(0.09)
(0.11)
(0.09)
(0.07)
(0.08)
(0.13)
(0.11)
(0.05)
(0.08)
(0.08)
(0.06)
(0.07)
(0.10)
(0.13)
(0.10)
(0.07)
(0.08)
(0.08)
(0.07)
(0.10)
(0.09)
(0.08)
(0.09)
(0.09)
(0.02)
12.2
0.8
3.4
6.0
0.4
3.5
2.1
8.7
1.5
0.3
0.0
6.1
4.8
0.0
2.1
0.0
0.0
0.0
4.4
9.1
2.1
0.5
1.2
0.0
2.1
3.0
5.0
0.0
9.5
5.2
3.1
(0.75)
(0.46)
(0.66)
(0.64)
(0.30)
(0.74)
(0.53)
(1.10)
(0.51)
(0.21)
(0.05)
(0.86)
(0.66)
(0.05)
(0.60)
(0.08)
(0.02)
(0.05)
(0.81)
(0.95)
(0.58)
(0.26)
(0.37)
(0.07)
(0.39)
(0.63)
(0.85)
(0.08)
(0.84)
(0.81)
(0.11)
Partners
Bottom
quarter
Change in the
science score
per unit of this
index
Increased likelihood of
students in the bottom
quarter of this index
scoring in the bottom
quarter of the national
science performance
distribution
Argentina
Azerbaijan
Brazil
Bulgaria
Chile
Colombia
Croatia
Estonia
Hong Kong-China
Indonesia
Israel
Jordan
Kyrgyzstan
Latvia
Liechtenstein
Lithuania
Macao-China
Montenegro
Qatar
Romania
Russian Federation
Serbia
Slovenia
Chinese Taipei
Thailand
Tunisia
Uruguay
406
380
401
441
439
401
494
524
533
395
460
418
333
486
496
476
520
429
342
417
482
453
510
539
413
387
428
(6.3)
(3.5)
(3.6)
(6.8)
(3.8)
(4.6)
(3.3)
(4.0)
(3.3)
(8.7)
(4.9)
(4.3)
(4.6)
(3.9)
(10.9)
(4.1)
(2.9)
(2.8)
(2.3)
(5.3)
(4.5)
(3.7)
(2.6)
(4.3)
(3.4)
(4.3)
(4.3)
400
386
390
435
436
393
495
540
541
396
456
424
332
499
516
488
512
416
355
417
481
438
524
525
417
389
430
(6.94)
(3.60)
(3.40)
(6.36)
(5.68)
(3.97)
(3.47)
(3.33)
(3.93)
(6.80)
(5.35)
(3.96)
(3.77)
(3.67)
(9.73)
(3.38)
(3.13)
(2.77)
(2.92)
(4.89)
(4.16)
(4.54)
(3.68)
(4.59)
(3.16)
(4.14)
(4.02)
393
385
387
440
436
386
495
531
536
392
458
429
319
489
520
493
507
410
355
422
475
432
523
530
421
386
434
(6.96)
(3.64)
(4.68)
(7.67)
(5.28)
(6.29)
(3.76)
(3.70)
(3.76)
(5.39)
(4.60)
(3.56)
(3.19)
(3.87)
(8.90)
(4.09)
(2.98)
(2.49)
(3.11)
(4.91)
(4.42)
(4.33)
(4.79)
(4.12)
(2.90)
(3.52)
(4.14)
382
384
389
436
446
374
490
532
560
392
465
421
317
486
562
499
505
397
357
420
480
423
527
537
432
388
437
(6.8)
(3.2)
(4.8)
(6.8)
(6.6)
(4.6)
(3.5)
(4.0)
(4.0)
(4.0)
(5.0)
(3.8)
(3.4)
(4.9)
(10.6)
(4.4)
(2.7)
(2.8)
(2.8)
(5.7)
(4.5)
(4.2)
(3.4)
(3.8)
(3.1)
(3.5)
(5.1)
-9.9
2.4
-3.9
0.2
2.1
-11.1
-0.8
1.8
9.9
-1.7
3.4
1.4
-7.3
-0.4
22.1
10.2
-4.9
-11.4
4.7
1.2
0.1
-11.4
8.7
1.3
8.9
-0.6
6.0
(2.33)
(1.32)
(1.93)
(2.32)
(1.88)
(2.05)
(1.62)
(1.78)
(1.66)
(3.15)
(1.60)
(1.64)
(2.04)
(1.95)
(4.27)
(2.07)
(1.43)
(1.45)
(1.01)
(1.82)
(1.70)
(1.61)
(1.67)
(1.36)
(1.47)
(1.62)
(2.22)
0.8
1.2
0.7
1.0
0.9
0.7
0.9
1.2
1.1
1.1
1.0
1.3
1.0
1.1
1.5
1.2
0.8
0.6
1.2
1.2
0.9
0.6
1.1
0.9
1.2
1.1
0.9
(0.09)
(0.10)
(0.07)
(0.09)
(0.08)
(0.11)
(0.06)
(0.09)
(0.08)
(0.08)
(0.08)
(0.08)
(0.09)
(0.08)
(0.34)
(0.08)
(0.08)
(0.05)
(0.07)
(0.13)
(0.08)
(0.06)
(0.07)
(0.05)
(0.10)
(0.10)
(0.07)
1.0
0.2
0.2
0.0
0.1
1.6
0.0
0.0
0.9
0.0
0.1
0.0
0.6
0.0
6.9
1.0
0.3
2.0
0.4
0.0
0.0
1.7
0.6
0.0
0.8
0.0
0.3
(0.46)
(0.17)
(0.16)
(0.04)
(0.10)
(0.56)
(0.04)
(0.07)
(0.32)
(0.15)
(0.12)
(0.06)
(0.33)
(0.04)
(2.52)
(0.38)
(0.18)
(0.51)
(0.19)
(0.07)
(0.03)
(0.50)
(0.24)
(0.04)
(0.26)
(0.04)
(0.24)
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
69
Appendix B – dATA TABLES
Table 14 [Part 1/2] Index of student information on science-related careers
Results based on students' self-reports
Index of student information on science-related careers
Females
Gender
difference
(M - F)
Bottom
quarter
Second
quarter
Third
quarter
Top
quarter
Mean
index
S.E.
Mean
index
S.E.
Mean
index
S.E.
Dif.
S.E.
Mean
index
S.E.
Mean
index
S.E.
Mean
index
S.E.
Mean
index
S.E.
OECD
Males
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
OECD average
0.18
-0.10
-0.23
0.28
-0.10
-0.11
0.13
0.00
0.02
0.33
-0.03
-0.06
0.00
0.07
-0.39
-0.33
-0.10
-0.44
-0.35
0.14
-0.11
0.31
0.40
-0.05
0.01
-0.13
0.04
0.27
-0.01
0.35
0.00
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.03)
(0.02)
(0.02)
(0.00)
0.13
-0.08
-0.22
0.28
-0.16
-0.10
0.06
-0.02
0.07
0.40
0.00
0.03
-0.04
0.14
-0.31
-0.27
-0.07
-0.34
-0.27
0.10
-0.02
0.30
0.38
-0.02
-0.01
-0.08
0.07
0.27
0.02
0.36
0.02
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.03)
(0.02)
(0.02)
(0.03)
(0.02)
(0.03)
(0.02)
(0.04)
(0.02)
(0.02)
(0.00)
0.23
-0.12
-0.24
0.28
-0.02
-0.11
0.19
0.03
-0.03
0.27
-0.06
-0.16
0.04
0.00
-0.47
-0.40
-0.13
-0.54
-0.43
0.17
-0.21
0.31
0.42
-0.07
0.02
-0.18
0.01
0.28
-0.03
0.34
-0.02
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.03)
(0.01)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.04)
(0.03)
(0.03)
(0.00)
-0.10
0.03
0.02
0.01
-0.14
0.01
-0.13
-0.05
0.10
0.14
0.05
0.19
-0.08
0.14
0.17
0.14
0.06
0.20
0.16
-0.08
0.19
-0.01
-0.04
0.05
-0.03
0.09
0.07
-0.01
0.05
0.02
0.04
(0.03)
(0.03)
(0.02)
(0.02)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.04)
(0.03)
(0.03)
(0.03)
(0.02)
(0.02)
(0.03)
(0.03)
(0.03)
(0.04)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.04)
(0.03)
(0.03)
(0.01)
-1.15
-1.24
-1.44
-1.03
-1.34
-1.18
-0.93
-1.27
-1.12
-1.03
-1.01
-1.17
-1.37
-0.98
-1.48
-1.37
-1.33
-1.89
-1.66
-1.12
-1.33
-0.88
-0.75
-1.27
-1.18
-1.42
-1.11
-1.18
-1.26
-0.97
-1.22
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.00)
-0.10
-0.42
-0.48
-0.03
-0.33
-0.44
-0.07
-0.23
-0.25
0.13
-0.24
-0.40
-0.32
-0.13
-0.63
-0.57
-0.42
-0.84
-0.54
-0.16
-0.48
0.02
0.17
-0.33
-0.30
-0.43
-0.21
0.01
-0.34
0.05
-0.28
(0.00)
(0.01)
(0.00)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
0.59
0.18
0.06
0.65
0.24
0.17
0.45
0.33
0.29
0.71
0.21
0.22
0.38
0.28
-0.21
-0.09
0.20
-0.17
-0.01
0.52
0.18
0.64
0.75
0.28
0.30
0.23
0.31
0.68
0.36
0.70
0.31
(0.00)
(0.01)
(0.00)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.00)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.00)
1.36
1.08
0.93
1.53
1.04
1.04
1.08
1.19
1.17
1.53
0.92
1.09
1.31
1.12
0.77
0.69
1.15
1.13
0.82
1.31
1.19
1.45
1.42
1.13
1.21
1.11
1.16
1.58
1.22
1.61
1.18
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
Partners
All students
Argentina
Azerbaijan
Brazil
Bulgaria
Chile
Colombia
Croatia
Estonia
Hong Kong-China
Indonesia
Israel
Jordan
Kyrgyzstan
Latvia
Liechtenstein
Lithuania
Macao-China
Montenegro
Qatar
Romania
Russian Federation
Serbia
Slovenia
Chinese Taipei
Thailand
Tunisia
Uruguay
-0.49
0.36
0.33
0.25
0.26
-0.02
0.02
-0.01
0.23
0.34
0.20
0.44
0.30
0.05
0.10
0.23
-0.12
-0.03
0.54
0.06
0.39
0.13
0.03
0.06
0.26
0.41
-0.23
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.01)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.05)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.01)
(0.01)
(0.01)
(0.02)
(0.02)
(0.02)
-0.40
0.43
0.34
0.22
0.23
-0.02
0.01
0.01
0.30
0.25
0.26
0.55
0.32
0.10
0.10
0.21
-0.02
0.07
0.67
0.14
0.38
0.20
0.05
0.09
0.28
0.51
-0.17
(0.04)
(0.03)
(0.03)
(0.03)
(0.03)
(0.04)
(0.02)
(0.02)
(0.02)
(0.04)
(0.04)
(0.02)
(0.03)
(0.02)
(0.09)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.03)
-0.56
0.29
0.32
0.28
0.29
-0.03
0.03
-0.03
0.15
0.43
0.13
0.34
0.28
0.01
0.10
0.25
-0.23
-0.14
0.42
-0.02
0.41
0.05
0.00
0.02
0.25
0.33
-0.29
(0.04)
(0.03)
(0.02)
(0.03)
(0.03)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.03)
(0.03)
(0.02)
(0.06)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.03)
0.16
0.13
0.02
-0.06
-0.06
0.01
-0.02
0.04
0.15
-0.17
0.13
0.21
0.04
0.09
0.00
-0.03
0.21
0.21
0.25
0.17
-0.03
0.15
0.06
0.07
0.03
0.18
0.11
(0.04)
(0.04)
(0.03)
(0.04)
(0.03)
(0.04)
(0.02)
(0.03)
(0.03)
(0.05)
(0.05)
(0.03)
(0.03)
(0.03)
(0.12)
(0.03)
(0.03)
(0.03)
(0.03)
(0.03)
(0.02)
(0.03)
(0.03)
(0.02)
(0.03)
(0.03)
(0.03)
-1.95
-0.90
-0.99
-1.13
-1.04
-1.09
-1.06
-1.13
-0.79
-0.85
-1.39
-0.83
-0.96
-1.04
-1.07
-0.77
-1.24
-1.29
-1.00
-1.03
-0.68
-1.04
-1.02
-1.02
-0.80
-0.86
-1.65
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.03)
(0.02)
(0.02)
(0.02)
(0.06)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
-0.89
0.04
0.04
-0.03
-0.09
-0.44
-0.26
-0.28
-0.03
0.05
-0.03
0.22
0.00
-0.19
-0.20
-0.01
-0.38
-0.40
0.30
-0.28
0.14
-0.20
-0.26
-0.18
0.06
0.13
-0.56
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.02)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
-0.15
0.69
0.71
0.64
0.61
0.20
0.27
0.25
0.56
0.66
0.67
0.80
0.64
0.31
0.37
0.53
0.19
0.28
0.94
0.31
0.67
0.40
0.27
0.34
0.63
0.78
0.14
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.01)
(0.00)
(0.01)
(0.03)
(0.01)
(0.00)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
1.05
1.63
1.55
1.53
1.55
1.25
1.14
1.10
1.16
1.49
1.53
1.57
1.52
1.13
1.30
1.17
0.95
1.28
1.93
1.25
1.44
1.36
1.12
1.08
1.16
1.60
1.14
(0.03)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.01)
(0.01)
(0.02)
(0.06)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.01)
(0.02)
(0.01)
(0.02)
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
70
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 14 [Part 2/2] Index of student information on science-related careers
Results based on students' self-reports
Performance on the science scale. by national quarters
of this index
Third
quarter
Top
quarter
Explained variance in
student performance
(r-squared x 100)
Mean
score
S.E.
Mean
score
S.E.
Mean
score
S.E.
Mean
score
S.E.
Effect
S.E.
Ratio
S.E.
Percentage
S.E.
OECD
Second
quarter
Australia
Austria
Belgium
Canada
Czech Republic
Denmark
Finland
France
Germany
Greece
Hungary
Iceland
Ireland
Italy
Japan
Korea
Luxembourg
Mexico
Netherlands
New Zealand
Norway
Poland
Portugal
Slovak Republic
Spain
Sweden
Switzerland
Turkey
United Kingdom
United States
OECD average
506
508
512
525
516
488
557
478
508
474
507
467
498
471
527
515
482
405
517
520
487
503
463
495
472
497
498
395
508
487
493
(2.9)
(4.3)
(3.3)
(2.7)
(4.1)
(4.2)
(3.1)
(4.2)
(6.1)
(4.1)
(3.6)
(3.0)
(4.4)
(2.6)
(5.0)
(4.1)
(2.7)
(2.5)
(3.7)
(3.6)
(4.1)
(3.1)
(4.0)
(4.4)
(3.1)
(4.4)
(4.0)
(3.2)
(3.5)
(4.6)
(0.7)
531
518
526
540
520
499
569
508
527
467
504
490
517
485
529
510
498
418
527
534
497
499
481
492
490
509
516
417
522
503
505
(3.44)
(4.28)
(3.39)
(2.09)
(5.21)
(4.28)
(3.27)
(4.01)
(4.58)
(4.51)
(4.12)
(2.99)
(3.65)
(2.67)
(4.66)
(3.60)
(2.54)
(2.89)
(3.97)
(3.47)
(3.33)
(4.00)
(3.73)
(4.87)
(3.14)
(3.69)
(3.78)
(3.76)
(3.64)
(5.40)
(0.70)
533
518
523
537
521
501
563
510
532
475
503
506
512
479
542
529
491
421
538
538
498
492
472
492
496
514
522
426
518
480
506
(3.69)
(5.71)
(3.48)
(2.99)
(4.41)
(4.39)
(4.00)
(4.11)
(4.51)
(4.41)
(3.91)
(3.24)
(4.95)
(2.49)
(3.90)
(4.13)
(2.90)
(2.93)
(4.85)
(4.17)
(3.77)
(3.63)
(3.91)
(3.91)
(3.35)
(3.12)
(4.04)
(4.82)
(3.58)
(4.96)
(0.73)
551
506
500
544
517
504
565
501
526
481
503
510
514
468
526
535
479
399
537
540
483
500
483
482
500
503
512
461
520
493
505
(4.0)
(5.1)
(3.2)
(2.7)
(4.5)
(4.6)
(3.3)
(5.2)
(4.4)
(4.1)
(4.8)
(3.3)
(4.4)
(3.5)
(4.6)
(5.8)
(3.2)
(4.7)
(5.8)
(4.1)
(3.8)
(3.4)
(4.1)
(4.4)
(3.6)
(3.9)
(4.6)
(6.8)
(3.7)
(5.9)
(0.8)
16.6
0.5
-2.5
7.1
1.9
8.5
5.1
11.0
8.4
4.1
-2.1
19.9
7.0
-2.4
2.2
10.6
-0.5
-2.6
8.9
8.7
-0.1
-0.5
7.2
-4.8
12.1
5.2
7.1
21.8
5.4
1.0
5.5
(1.25)
(1.98)
(1.62)
(1.06)
(1.65)
(1.86)
(1.70)
(2.01)
(2.01)
(1.68)
(2.34)
(1.66)
(1.69)
(1.45)
(1.72)
(2.46)
(1.54)
(1.38)
(2.36)
(1.72)
(1.56)
(1.51)
(1.81)
(1.87)
(1.23)
(1.45)
(1.79)
(2.27)
(1.49)
(2.03)
(0.32)
1.5
1.0
1.1
1.2
1.0
1.1
1.1
1.4
1.3
1.0
0.9
1.6
1.2
1.1
1.1
1.1
1.1
1.0
1.2
1.1
1.1
0.8
1.3
0.8
1.4
1.2
1.2
1.6
1.1
1.0
1.1
(0.08)
(0.07)
(0.06)
(0.06)
(0.10)
(0.09)
(0.07)
(0.09)
(0.09)
(0.08)
(0.09)
(0.10)
(0.08)
(0.06)
(0.07)
(0.08)
(0.07)
(0.06)
(0.09)
(0.07)
(0.07)
(0.06)
(0.09)
(0.08)
(0.10)
(0.08)
(0.09)
(0.11)
(0.07)
(0.08)
(0.01)
3.0
0.0
0.1
0.6
0.0
0.7
0.2
1.2
0.7
0.2
0.0
3.9
0.7
0.0
0.0
1.0
0.0
0.1
0.9
0.7
0.0
0.0
0.5
0.3
1.7
0.3
0.4
8.4
0.3
0.0
0.9
(0.43)
(0.04)
(0.08)
(0.18)
(0.07)
(0.32)
(0.17)
(0.45)
(0.30)
(0.17)
(0.08)
(0.65)
(0.32)
(0.05)
(0.07)
(0.46)
(0.03)
(0.17)
(0.50)
(0.27)
(0.03)
(0.03)
(0.26)
(0.20)
(0.35)
(0.18)
(0.23)
(1.35)
(0.15)
(0.06)
(0.07)
Partners
Bottom
quarter
Change in the
science score
per unit of this
index
Increased likelihood of
students in the bottom
quarter of this index
scoring in the bottom
quarter of the national
science performance
distribution
Argentina
Azerbaijan
Brazil
Bulgaria
Chile
Colombia
Croatia
Estonia
Hong Kong-China
Indonesia
Israel
Jordan
Kyrgyzstan
Latvia
Liechtenstein
Lithuania
Macao-China
Montenegro
Qatar
Romania
Russian Federation
Serbia
Slovenia
Chinese Taipei
Thailand
Tunisia
Uruguay
392
(7.0)
380
(4.0)
397
(3.7)
433
(5.6)
434
(4.5)
390
(5.6)
481
(3.3)
542
(4.0)
538
(3.7)
399
(8.5)
453
(5.2)
430
(4.1)
318
(4.1)
502
(3.9)
529 (10.5)
484
(3.9)
511
(2.5)
420
(2.5)
351
(2.1)
422
(4.6)
484
(4.1)
436
(3.3)
521
(2.9)
509
(4.3)
419
(3.1)
394
(3.8)
432
(4.2)
407 (5.90) 405 (6.08) 383
389 (3.62) 385 (3.09) 381
396 (3.89) 391 (3.85) 384
452 (7.04) 442 (6.62) 425
441 (5.51) 441 (4.75) 443
392 (4.24) 393 (4.38) 381
494 (3.32) 502 (3.09) 496
542 (3.88) 532 (3.66) 511
548 (3.74) 540 (4.04) 543
397 (5.93) 391 (5.50) 387
464 (4.76) 464 (4.83) 462
419 (3.64) 421 (3.37) 424
331 (3.56) 332 (3.55) 321
495 (4.15) 484 (5.38) 478
516 (12.22) 520 (10.93) 531
483 (3.60) 489 (3.60) 500
512 (2.62) 513 (2.81) 508
424 (2.98) 412 (2.96) 397
347 (2.19) 355 (2.45) 354
426 (4.46) 424 (6.75) 405
485 (4.31) 476 (4.12) 474
445 (3.61) 440 (3.94) 426
532 (2.78) 525 (3.51) 509
537 (4.09) 547 (3.76) 538
423 (3.16) 421 (3.30) 419
385 (4.28) 381 (4.01) 388
438 (4.32) 437 (4.51) 419
(7.7)
(3.2)
(4.3)
(7.2)
(5.5)
(4.9)
(3.9)
(4.0)
(3.7)
(4.8)
(5.4)
(4.1)
(4.0)
(3.7)
(9.2)
(3.6)
(3.5)
(2.9)
(2.4)
(4.5)
(4.8)
(4.4)
(3.9)
(4.7)
(4.0)
(4.2)
(4.5)
-4.1
0.1
-5.0
-4.5
2.7
-3.6
7.3
-12.4
1.2
-5.7
4.2
-1.0
0.7
-10.1
1.4
8.2
0.7
-9.1
3.2
-6.6
-3.2
-4.9
-4.3
14.2
-0.7
-1.5
-3.7
(2.34)
(1.48)
(1.59)
(2.22)
(1.47)
(2.14)
(1.49)
(1.87)
(2.06)
(2.57)
(1.67)
(1.81)
(1.75)
(2.01)
(5.13)
(1.42)
(1.62)
(1.25)
(1.00)
(2.01)
(1.44)
(1.42)
(2.09)
(1.53)
(1.82)
(1.61)
(1.94)
1.0
1.1
0.8
1.0
1.1
1.0
1.2
0.8
1.1
0.9
1.1
0.9
1.2
0.7
0.9
1.1
1.0
0.8
1.0
0.8
0.9
0.9
1.0
1.5
0.9
0.7
0.9
(0.10)
(0.09)
(0.07)
(0.07)
(0.07)
(0.08)
(0.07)
(0.08)
(0.09)
(0.07)
(0.09)
(0.09)
(0.08)
(0.07)
(0.22)
(0.07)
(0.09)
(0.06)
(0.06)
(0.10)
(0.06)
(0.07)
(0.09)
(0.08)
(0.07)
(0.09)
(0.08)
0.2
0.0
0.3
0.2
0.1
0.2
0.6
1.8
0.0
0.6
0.2
0.0
0.0
1.1
0.0
0.5
0.0
1.4
0.2
0.6
0.1
0.3
0.2
1.7
0.0
0.0
0.2
(0.28)
(0.06)
(0.21)
(0.21)
(0.11)
(0.20)
(0.24)
(0.53)
(0.05)
(0.53)
(0.15)
(0.05)
(0.05)
(0.42)
(0.29)
(0.17)
(0.04)
(0.39)
(0.12)
(0.34)
(0.08)
(0.19)
(0.15)
(0.38)
(0.04)
(0.08)
(0.20)
Note: Values that are statistically significant are indicated in bold.
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
71
Appendix B – dATA TABLES
Table 15 [Part 1/2] Percentage of students expecting a particular career at age 30, by field of science
Computer sciences
OECD
Males
%
S.E.
%
Australia
14.3
(1.0)
Austria
13.5
Belgium
Males
Heath sciences
Females
Males
Life sciences
Females
Males
Females
S.E.
%
S.E.
%
S.E.
%
S.E.
%
S.E.
%
S.E.
%
S.E.
1.3
(0.3)
32.0
(1.4)
6.8
(0.5)
22.9
(1.3)
40.1
(1.3)
10.5
(0.8)
16.9
(1.1)
(2.1)
0.8
(0.4)
19.3
(3.4)
9.2
(1.4)
13.7
(2.2)
33.2
(2.2)
4.6
(1.0)
5.6
(1.2)
18.0
(1.2)
2.0
(0.5)
34.5
(1.7)
10.9
(1.1)
16.2
(1.2)
31.1
(1.7)
5.6
(0.8)
4.8
(0.5)
Canada
15.2
(1.3)
0.6
(0.2)
30.5
(1.3)
5.9
(0.6)
27.5
(1.3)
52.8
(1.1)
7.1
(0.6)
7.0
(0.7)
Czech Republic
37.8
(3.3)
1.1
(0.4)
19.3
(3.2)
17.4
(4.1)
9.0
(1.4)
31.2
(2.9)
7.0
(1.3)
12.3
(1.6)
5.4
(1.0)
0.0
(0.0)
43.8
(2.3)
8.6
(1.3)
20.6
(1.9)
43.4
(2.5)
6.8
(1.2)
9.6
(1.3)
Finland
16.7
(1.8)
0.6
(0.4)
23.5
(2.3)
7.6
(1.3)
19.9
(2.5)
47.4
(2.5)
7.0
(1.4)
5.3
(1.1)
France
12.1
(1.4)
1.1
(0.4)
25.1
(1.9)
6.2
(1.0)
18.1
(1.7)
57.5
(1.8)
8.8
(1.3)
4.0
(0.7)
Germany
16.5
(1.8)
2.2
(0.7)
22.7
(2.1)
9.7
(1.3)
13.5
(1.9)
39.3
(2.2)
8.2
(1.3)
9.6
(1.7)
Greece
15.1
(1.6)
2.8
(0.8)
27.2
(2.3)
16.5
(1.7)
18.2
(1.9)
30.8
(1.7)
6.2
(1.0)
8.4
(0.9)
Hungary
37.2
(3.9)
6.0
(1.5)
29.4
(2.9)
9.1
(1.6)
14.3
(2.2)
42.2
(2.3)
2.7
(0.9)
9.7
(1.7)
Iceland
11.7
(1.3)
0.4
(0.2)
23.3
(1.7)
15.4
(1.4)
25.0
(2.1)
39.1
(1.9)
5.1
(1.1)
5.0
(0.8)
Ireland
8.7
(1.2)
1.9
(0.5)
42.9
(2.1)
7.3
(1.2)
24.6
(2.1)
45.8
(2.2)
6.7
(1.1)
9.5
(1.2)
Italy
9.7
(0.9)
1.1
(0.2)
40.0
(2.8)
12.1
(1.4)
21.7
(2.5)
42.5
(1.5)
7.1
(1.7)
8.4
(0.8)
Japan
m
m
m
m
m
m
m
m
m
m
m
m
Korea
18.9
(2.3)
2.7
(1.0)
24.3
(2.5)
10.6
(1.5)
21.4
(1.6)
38.2
(2.4)
8.4
(1.2)
6.6
(1.9)
Luxembourg
16.4
(1.6)
1.9
(0.6)
31.0
(2.0)
13.9
(1.4)
15.9
(1.5)
33.5
(2.0)
6.3
(1.2)
5.2
(0.9)
Mexico
13.3
(0.9)
7.2
(0.7)
39.2
(1.4)
9.9
(0.9)
22.6
(1.2)
39.4
(1.3)
10.8
(0.9)
16.3
(1.1)
Netherlands
12.3
(2.1)
0.3
(0.2)
26.8
(2.4)
6.6
(1.0)
25.9
(2.8)
34.8
(2.0)
1.8
(0.8)
4.5
(0.7)
New Zealand
15.0
(2.0)
0.2
(0.2)
26.7
(1.9)
10.1
(1.1)
31.0
(2.3)
49.4
(2.2)
6.3
(1.1)
10.6
(1.0)
Norway
14.1
(1.9)
2.0
(0.6)
49.6
(2.6)
16.5
(1.8)
15.9
(1.8)
39.5
(1.9)
5.4
(1.3)
4.3
(0.9)
Poland
33.1
(1.9)
5.1
(0.8)
11.7
(1.0)
14.1
(1.4)
11.6
(1.1)
43.9
(1.7)
5.0
(0.7)
5.1
(0.8)
Portugal
19.3
(1.5)
1.4
(0.3)
31.6
(2.1)
10.7
(1.1)
19.5
(1.4)
46.8
(1.6)
8.4
(0.9)
8.8
(0.9)
Slovak Republic
43.6
(3.7)
2.2
(0.7)
18.8
(3.2)
8.7
(1.9)
10.0
(1.6)
39.8
(2.7)
3.0
(0.8)
5.2
(1.1)
Spain
23.3
(1.6)
2.2
(0.5)
34.4
(1.7)
12.0
(1.0)
17.8
(1.4)
46.0
(1.5)
7.8
(0.9)
9.0
(1.0)
Sweden
16.3
(1.8)
0.3
(0.2)
21.1
(2.2)
12.0
(1.5)
16.0
(1.8)
40.0
(2.2)
2.4
(0.7)
2.8
(0.8)
Switzerland
22.2
(1.6)
2.9
(0.7)
27.7
(1.8)
7.4
(1.1)
9.6
(1.0)
41.4
(1.9)
6.1
(1.0)
11.8
(1.1)
2.7
(1.0)
2.0
(1.0)
51.1
(2.3)
19.2
(2.3)
30.0
(2.3)
43.5
(2.8)
4.0
(0.8)
6.5
(1.4)
14.3
(1.2)
2.0
(0.5)
31.4
(1.7)
4.4
(0.7)
28.2
(1.6)
41.3
(1.7)
6.9
(1.1)
10.6
(1.2)
9.0
(0.9)
1.1
(0.3)
31.3
(1.5)
4.2
(0.7)
31.4
(1.5)
54.3
(1.6)
7.1
(0.8)
8.0
(0.9)
OECD average
17.4
(0.3)
1.9
(0.1)
30.0
(0.4)
10.4
(0.3)
19.7
(0.3)
41.7
(0.4)
6.3
(0.2)
8.0
(0.2)
Argentina
11.8
(1.4)
2.5
(0.7)
37.4
(3.5)
10.1
(1.8)
21.5
(2.4)
44.5
(2.5)
11.4
(2.0)
10.1
(1.1)
Azerbaijan
11.0
(1.6)
2.6
(1.0)
25.0
(2.8)
3.9
(0.9)
43.0
(3.1)
67.8
(2.4)
9.6
(2.7)
3.6
(1.0)
7.2
(1.1)
1.1
(0.4)
2.9
(0.6)
3.8
(0.8)
35.9
(2.0)
59.9
(1.4)
15.6
(1.5)
11.7
(1.3)
26.0
(2.2)
24.8
(1.8)
9.9
(1.3)
5.2
(1.0)
50.9
(2.4)
55.5
(2.4)
3.7
(0.9)
3.2
(0.8)
8.1
(1.0)
0.9
(0.3)
39.3
(2.0)
10.7
(1.0)
26.9
(1.5)
54.9
(2.0)
6.6
(0.9)
8.5
(1.0)
14.3
(1.9)
4.9
(1.2)
33.7
(2.5)
9.8
(1.4)
26.4
(1.4)
54.7
(1.5)
10.1
(1.5)
12.7
(1.0)
Croatia
9.4
(1.9)
2.3
(0.8)
15.0
(1.9)
7.9
(1.5)
16.7
(3.2)
49.2
(3.7)
9.9
(2.1)
11.8
(1.6)
Estonia
39.1
(2.6)
3.8
(0.9)
28.4
(2.3)
25.2
(2.0)
6.5
(1.0)
32.7
(2.6)
7.3
(1.2)
7.1
(1.1)
Hong Kong-China
17.6
(1.7)
2.7
(1.1)
19.7
(2.0)
5.0
(1.1)
34.7
(2.0)
46.6
(2.0)
3.6
(0.8)
2.6
(0.8)
2.1
(0.9)
2.9
(1.7)
20.8
(7.4)
10.4
(2.3)
44.0
(8.9)
58.0
(3.3)
10.3
(3.3)
11.2
(2.6)
Israel
31.5
(3.2)
6.4
(1.1)
3.6
(1.3)
5.1
(0.8)
34.4
(2.9)
45.9
(2.3)
9.7
(1.9)
9.5
(1.3)
Jordan
5.9
(0.7)
6.2
(0.8)
42.0
(2.2)
27.1
(1.7)
29.0
(1.7)
36.5
(1.5)
1.5
(0.3)
5.4
(0.7)
Kyrgyzstan
18.1
(2.2)
3.8
(0.7)
10.6
(2.0)
0.8
(0.3)
55.9
(3.2)
80.7
(1.5)
3.7
(1.0)
1.8
(0.5)
Latvia
31.4
(2.1)
3.6
(1.1)
36.7
(2.6)
34.2
(2.3)
8.7
(1.4)
32.6
(2.4)
4.8
(1.3)
5.1
(1.2)
Liechtenstein
26.6
(7.6)
2.9
(2.9)
20.6
(7.0)
5.8
(4.0)
8.7
(4.9)
32.0
(7.9)
0.0
(0.0)
14.9
(6.5)
Lithuania
35.0
(2.3)
2.7
(0.7)
25.2
(2.3)
15.1
(1.8)
11.1
(1.4)
39.4
(2.4)
13.3
(1.5)
16.3
(1.8)
Macao-China
17.9
(2.0)
3.3
(1.4)
22.9
(2.8)
4.8
(1.1)
31.6
(2.5)
49.5
(2.5)
7.7
(1.3)
7.9
(1.8)
5.6
(1.3)
5.5
(1.2)
13.1
(1.7)
8.6
(1.5)
26.1
(2.6)
37.6
(2.8)
13.2
(2.0)
14.9
(1.9)
Qatar
35.0
(2.9)
5.6
(1.1)
35.6
(3.1)
15.7
(2.2)
16.6
(2.0)
46.4
(2.5)
2.0
(0.5)
11.5
(1.7)
Romania
42.6
(2.7)
9.5
(1.2)
21.8
(1.9)
8.5
(1.4)
11.0
(1.6)
46.1
(2.4)
5.9
(1.3)
6.7
(1.3)
Russian Federation
12.9
(1.5)
1.4
(0.6)
23.9
(3.1)
8.4
(1.8)
22.9
(3.5)
51.5
(4.3)
4.0
(1.1)
10.4
(1.8)
Serbia
21.6
(1.5)
0.5
(0.3)
22.8
(1.4)
7.1
(1.3)
17.7
(1.6)
46.2
(2.0)
5.7
(1.0)
11.1
(1.3)
Slovenia
13.8
(2.1)
5.0
(0.7)
35.0
(2.3)
13.5
(1.2)
30.7
(2.5)
40.4
(1.9)
5.0
(1.4)
6.6
(0.7)
Chinese Taipei
41.0
(1.8)
11.7
(1.9)
16.2
(1.4)
5.8
(1.0)
18.5
(1.6)
31.9
(4.0)
9.9
(0.8)
10.7
(1.9)
Thailand
12.2
(2.4)
4.1
(0.8)
21.3
(1.6)
11.7
(1.4)
34.6
(2.0)
61.9
(2.2)
5.2
(1.1)
2.5
(0.5)
Tunisia
12.0
(1.7)
2.2
(0.5)
27.1
(2.3)
12.9
(1.2)
28.6
(2.5)
50.9
(1.5)
14.5
(3.1)
9.1
(1.8)
Uruguay
-0.23 (0.02) -0.17
(0.03)
-0.29 (0.03)
-1.65
(0.02)
-0.56
(0.01)
(0.01)
1.14
(0.02)
Denmark
Turkey
United Kingdom
United States
Partners
Engineering
Females
Brazil
Bulgaria
Chile
Colombia
Indonesia
Montenegro
m
m
0.11 (0.03)
m
m
0.14
Source: OECD PISA 2006 Database
72
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 15 [Part 2/2] Percentage of students expecting a particular career at age 30, by field of science
Nursing
Males
Partners
OECD
%
Physical sciences
Females
S.E.
%
Males
S.E.
%
Technician
Females
S.E.
%
Males
S.E.
%
Females
S.E.
%
S.E.
Australia
3.2
(0.4)
24.7
(1.0)
4.6
(0.5)
2.1
(0.4)
12.3
(0.9)
8.0
(0.7)
Austria
4.5
(1.1)
42.4
(2.5)
6.6
(1.2)
2.6
(0.8)
37.6
(3.7)
6.2
(1.3)
Belgium
6.9
(0.9)
43.9
(1.7)
6.7
(0.8)
2.3
(0.5)
11.4
(1.3)
4.4
(0.7)
Canada
4.0
(0.4)
22.6
(1.0)
6.0
(0.6)
5.2
(0.6)
9.8
(0.8)
6.0
(0.5)
Czech Republic
2.7
(0.9)
33.2
(3.3)
3.9
(0.9)
2.6
(0.7)
20.4
(2.4)
2.2
(0.8)
Denmark
3.5
(1.0)
28.4
(2.1)
3.7
(0.9)
2.1
(0.7)
15.9
(1.5)
7.6
(1.2)
Finland
4.5
(1.1)
29.1
(2.3)
9.9
(1.5)
2.9
(0.7)
17.9
(2.0)
7.1
(1.3)
France
7.3
(1.3)
21.5
(1.4)
8.1
(1.3)
2.9
(0.6)
20.5
(1.8)
6.5
(0.9)
Germany
5.3
(1.1)
26.7
(2.4)
9.8
(1.6)
4.3
(0.9)
24.1
(2.0)
8.3
(1.6)
Greece
2.6
(0.8)
26.3
(2.1)
14.3
(1.7)
13.4
(1.4)
16.4
(1.8)
1.8
(0.5)
Hungary
3.5
(0.8)
25.9
(2.9)
5.7
(1.1)
3.3
(0.9)
7.2
(1.2)
3.2
(1.2)
Iceland
8.1
(1.4)
29.2
(1.8)
9.8
(1.3)
2.8
(0.7)
17.0
(1.9)
8.1
(1.2)
Ireland
3.1
(0.6)
31.7
(1.6)
3.6
(0.8)
1.2
(0.4)
10.3
(1.4)
2.6
(0.6)
Italy
3.6
(0.5)
30.7
(1.9)
6.9
(1.4)
2.3
(0.4)
11.1
(1.3)
3.0
(0.5)
Japan
m
m
m
m
m
m
m
m
m
m
Korea
3.4
(0.8)
33.8
(2.9)
14.0
(1.6)
4.6
(1.0)
6.3
(1.2)
2.8
(0.9)
Luxembourg
6.2
(1.0)
36.5
(2.1)
8.0
(1.0)
3.8
(0.7)
16.2
(1.6)
5.3
(1.0)
Mexico
2.8
(0.5)
21.5
(1.4)
6.6
(0.8)
5.1
(0.6)
4.5
(0.7)
0.8
(0.3)
Netherlands
7.7
(1.5)
48.8
(2.0)
3.7
(0.9)
0.8
(0.3)
21.7
(2.4)
4.1
(0.8)
New Zealand
3.2
(0.8)
20.9
(1.7)
5.6
(1.1)
2.4
(0.6)
12.3
(1.6)
6.4
(1.0)
Norway
2.3
(0.6)
30.9
(1.9)
3.5
(0.9)
1.5
(0.6)
9.2
(1.6)
5.3
(0.8)
Poland
3.3
(0.6)
26.6
(1.6)
2.7
(0.5)
2.8
(0.5)
32.6
(1.7)
2.3
(0.5)
Portugal
5.5
(0.8)
25.1
(1.5)
3.6
(0.7)
1.5
(0.4)
12.1
(1.3)
5.7
(0.8)
Slovak Republic
5.0
(1.1)
36.1
(2.9)
5.3
(1.0)
6.7
(2.0)
14.4
(2.6)
1.3
(0.6)
Spain
3.6
(0.6)
25.6
(1.0)
4.5
(0.5)
3.4
(0.5)
8.6
(0.9)
1.8
(0.3)
Sweden
3.1
(0.8)
25.3
(2.0)
5.1
(1.0)
2.1
(0.6)
36.0
(2.2)
17.5
(2.2)
Switzerland
3.1
(0.7)
26.4
(1.6)
9.5
(1.1)
2.4
(0.6)
21.9
(1.4)
7.7
(1.0)
Turkey
2.9
(0.6)
26.2
(3.2)
1.6
(0.6)
1.7
(0.6)
7.7
(1.3)
0.7
(0.3)
United Kingdom
3.6
(0.7)
33.8
(1.9)
5.0
(0.7)
1.9
(0.4)
10.6
(1.3)
6.0
(0.9)
United States
3.6
(0.7)
26.0
(1.7)
4.6
(0.7)
1.3
(0.3)
13.0
(1.3)
5.1
(0.7)
OECD average
4.2
(0.2)
29.6
(0.4)
6.3
(0.2)
3.2
(0.1)
15.8
(0.3)
5.1
(0.2)
Argentina
4.5
(1.2)
24.8
(2.2)
5.1
(1.0)
4.7
(0.9)
8.1
(1.3)
3.2
(0.9)
Azerbaijan
2.9
(1.1)
18.6
(1.7)
4.2
(1.2)
2.7
(0.7)
4.1
(1.1)
0.8
(0.4)
Brazil
3.5
(1.2)
14.3
(1.1)
1.6
(0.5)
1.6
(0.3)
33.4
(2.1)
7.5
(0.8)
Bulgaria
2.4
(0.6)
3.1
(0.8)
4.3
(0.9)
7.1
(1.2)
2.8
(1.2)
1.1
(0.5)
Chile
4.9
(0.8)
19.4
(1.6)
6.4
(0.7)
3.4
(0.7)
7.7
(1.1)
2.1
(0.6)
Colombia
1.6
(0.4)
13.9
(1.2)
7.5
(1.2)
2.9
(0.6)
6.5
(1.0)
0.7
(0.3)
Croatia
2.9
(0.9)
21.1
(2.2)
4.6
(1.1)
2.1
(0.8)
41.6
(3.7)
5.8
(1.3)
Estonia
2.0
(0.6)
18.6
(1.8)
6.6
(1.3)
4.3
(1.0)
9.9
(1.5)
8.4
(1.1)
Hong Kong-China
5.8
(1.2)
33.8
(1.9)
9.2
(1.3)
4.0
(0.8)
9.3
(1.5)
5.3
(1.1)
Indonesia
6.4
(2.0)
10.3
(2.3)
3.6
(1.6)
4.4
(1.5)
12.8
(5.2)
2.6
(0.8)
Israel
3.7
(1.2)
27.3
(2.1)
10.3
(1.8)
3.9
(0.8)
6.7
(1.6)
1.7
(0.6)
Jordan
10.5
(1.2)
21.1
(1.6)
2.2
(0.4)
2.4
(0.5)
8.8
(1.1)
1.3
(0.4)
Kyrgyzstan
1.1
(0.5)
9.3
(1.2)
5.5
(1.3)
3.3
(0.6)
4.8
(1.4)
0.2
(0.1)
Latvia
3.0
(0.7)
17.0
(1.9)
3.4
(1.1)
4.5
(1.1)
11.7
(1.5)
2.9
(0.7)
Liechtenstein
0.0
(0.0)
32.2
(9.0)
3.0
(3.0)
6.0
(4.1)
41.1
(8.6)
6.2
(4.1)
Lithuania
2.7
(0.7)
18.4
(1.7)
8.0
(1.4)
7.0
(1.2)
4.8
(1.3)
1.1
(0.5)
Macao-China
4.1
(1.0)
24.8
(1.9)
9.2
(1.5)
6.5
(1.3)
6.4
(1.6)
3.2
(0.8)
Montenegro
18.8
(2.3)
17.7
(1.9)
4.4
(1.1)
4.5
(1.1)
18.5
(2.6)
11.1
(2.0)
Qatar
1.0
(0.4)
10.4
(1.3)
2.7
(0.7)
2.0
(0.4)
7.1
(1.3)
8.5
(2.0)
Romania
2.5
(0.8)
21.3
(1.9)
6.2
(1.1)
3.0
(0.6)
10.0
(1.9)
4.9
(1.3)
Russian Federation
0.7
(0.3)
17.7
(2.5)
2.7
(0.7)
1.3
(0.6)
32.8
(3.3)
9.3
(2.0)
Serbia
2.5
(0.6)
28.0
(1.6)
5.7
(0.9)
4.2
(0.9)
24.0
(1.6)
3.0
(0.9)
Slovenia
1.2
(0.6)
25.7
(1.7)
9.6
(1.6)
7.2
(0.8)
4.8
(1.1)
1.6
(0.4)
Chinese Taipei
1.7
(0.7)
31.9
(8.0)
6.4
(0.7)
4.6
(1.0)
5.9
(0.8)
3.1
(0.8)
Thailand
0.9
(0.4)
7.3
(1.0)
14.4
(1.4)
6.4
(1.0)
10.7
(1.2)
4.2
(0.6)
Tunisia
4.1
(0.7)
20.6
(1.5)
3.7
(0.8)
3.4
(0.7)
9.9
(1.8)
1.0
(0.4)
-0.23
(0.02)
-0.17
(0.03)
-0.29
(0.03)
0.11
(0.03)
-1.65
(0.02)
-0.56
(0.01)
Uruguay
m
m
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
73
Appendix B – dATA TABLES
Table 16 Performance difference between single and mixed schools by gender. before and after accounting the
socio-economic background of students and schools (ESCS)
Single-sex schools
Mixed-sex schools
Raw difference in score
(single - mixed)
Difference after
accounting for student
ESCS
Difference after
accounting for student
and school ESCS
Mean
S.E.
Mean
S.E.
Score dif.
S.E.
Score dif.
S.E.
Score dif.
Australia
560
(12.41)
520
(2.93)
41
(12.74)
22
(10.18)
-2
Chile
499
(15.97)
443
(5.57)
55
(17.34)
43
(13.55)
31
Colombia
411
(5.93)
393
(4.15)
18
(6.88)
3
(7.55)
-14
(8.04)
Hong Kong-China
573
(27.97)
543
(3.43)
30
(28.76)
20
(23.51)
-1
(17.58)
Ireland
526
(8.09)
498
(5.00)
28
(9.54)
14
(7.64)
-2
(7.07)
Israel
468
(16.41)
453
(5.70)
15
(17.16)
8
(15.59)
-2
(15.24)
Japan
549
(34.50)
531
(4.74)
17
(35.50)
5
(31.62)
-32
(23.41)
Jordan
400
(4.21)
512
(17.63)
-111
(16.91)
-80
(17.05)
-51
(18.96)
Korea
538
(6.07)
508
(7.21)
30
(9.56)
24
(8.42)
12
(7.75)
Macao-China
497
(4.24)
515
(1.99)
-18
(4.65)
-29
(4.91)
-58
(5.60)
New Zealand
541
(11.08)
524
(4.02)
18
(12.13)
5
(9.11)
-6
(8.89)
Qatar
324
(2.24)
337
(1.55)
-14
(2.87)
-16
(2.87)
-19
(2.82)
Chinese Taipei
662
(6.34)
535
(4.16)
127
(7.55)
111
(7.86)
73
(8.89)
Thailand
457
(5.07)
411
(3.37)
47
(6.11)
18
(6.48)
-7
(6.31)
United Kingdom
481
(23.12)
521
(2.98)
-41
(23.58)
-24
(13.15)
-14
(7.37)
(5.19)
S.E.
Males
(8.72)
(12.20)
Females
Australia
551
(6.40)
521
(2.63)
30
(6.89)
12
(5.26)
-2
Chile
410
(26.52)
427
(4.64)
-17
(27.51)
-28
(15.19)
-32
(8.76)
Colombia
416
(27.14)
383
(4.22)
33
(27.68)
8
(25.52)
-11
(23.48)
Hong Kong-China
567
(10.77)
532
(3.57)
36
(11.93)
21
(11.01)
-12
(14.35)
Ireland
522
(4.62)
497
(4.19)
24
(6.15)
12
(5.07)
4
(4.72)
Israel
460
(12.15)
450
(4.50)
10
(13.10)
12
(11.27)
18
(9.54)
Japan
522
(20.45)
531
(4.59)
-9
(21.42)
-18
(17.28)
-37
(12.01)
Jordan
434
(3.58)
444
(9.09)
-10
(10.08)
-6
(7.28)
-4
(6.52)
Korea
523
(5.41)
524
(7.18)
-1
(9.82)
2
(7.96)
6
(6.28)
Luxembourg
452
(4.23)
486
(1.85)
-34
(4.33)
-22
(4.27)
-1
(4.28)
Macao-China
540
(3.16)
505
(1.79)
36
(3.75)
32
(3.92)
26
(4.02)
New Zealand
558
(7.05)
518
(3.61)
40
(8.25)
24
(6.28)
13
(6.04)
Qatar
355
(1.99)
370
(1.62)
-15
(2.46)
-14
(2.51)
-17
(2.46)
Chinese Taipei
566
(32.75)
525
(5.01)
41
(34.06)
27
(27.32)
2
(14.88)
Thailand
486
(8.66)
425
(2.66)
61
(9.30)
28
(5.83)
5
(6.18)
Turkey
386
(7.78)
435
(4.29)
-49
(9.19)
-44
(7.28)
-36
(6.77)
United Kingdom
543
(16.41)
506
(2.95)
37
(17.05)
27
(12.03)
17
(8.11)
Source: OECD PISA 2006 Database.
74
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Appendix B – dATA TABLES
Table 17 [Part 1/2] Percentage of males and females doing homework for science. mathematics and reading
Out of school - Science
OECD
Males
Males
Out of school - Mathematics
Females
Males
Females
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
Australia
Austria
6.3
4.4
(0.3)
(0.4)
4.3
2.9
(0.2)
(0.4)
19.2
22.1
(0.8)
(1.3)
18.8
20.6
(0.8)
(1.0)
15.5
8.6
(0.6)
(0.6)
11.6
9.2
(0.5)
(0.7)
Belgium
5.6
(0.5)
4.4
(0.4)
18.1
(0.8)
23.8
(0.9)
10.4
(0.5)
8.4
(0.5)
Canada
8.2
(0.4)
9.2
(0.5)
23.7
(0.9)
35.0
(0.9)
16.5
(0.8)
18.0
(0.6)
Czech Republic
7.6
(0.6)
9.8
(0.7)
13.4
(0.9)
18.8
(1.1)
13.2
(0.7)
12.0
(0.8)
10.8
(0.7)
9.0
(0.7)
12.6
(0.8)
13.9
(1.0)
27.2
(1.1)
28.2
(1.1)
Finland
4.6
(0.5)
3.4
(0.4)
10.7
(0.8)
14.1
(0.9)
5.6
(0.5)
3.9
(0.4)
France
7.6
(0.6)
7.7
(0.6)
19.4
(1.2)
23.8
(1.1)
14.7
(0.8)
17.7
(0.9)
Germany
9.9
(0.7)
7.2
(0.5)
29.3
(1.0)
33.8
(1.0)
15.8
(0.9)
15.0
(0.8)
Greece
43.5
(1.3)
45.0
(1.3)
38.1
(1.2)
38.1
(1.2)
48.3
(1.6)
52.4
(1.4)
Hungary
18.6
(0.8)
17.8
(1.1)
27.0
(1.2)
33.8
(1.2)
22.6
(0.9)
25.1
(1.1)
Iceland
5.7
(0.6)
3.3
(0.4)
16.4
(0.9)
16.2
(0.8)
11.7
(0.7)
14.1
(0.8)
Ireland
5.3
(0.5)
3.7
(0.5)
19.5
(1.1)
20.0
(1.1)
12.6
(0.8)
12.4
(0.9)
Italy
12.1
(0.5)
8.9
(0.4)
41.5
(1.1)
46.5
(1.3)
18.1
(0.7)
16.6
(0.6)
Japan
5.0
(0.5)
2.6
(0.2)
7.6
(0.5)
5.1
(0.5)
14.1
(0.8)
12.5
(1.1)
Korea
23.3
(1.5)
20.9
(1.3)
19.2
(1.8)
19.6
(1.1)
50.8
(1.7)
52.2
(1.7)
9.4
(0.6)
7.3
(0.5)
17.6
(0.9)
21.1
(0.9)
15.6
(0.8)
14.8
(0.7)
19.7
(1.0)
17.5
(0.8)
39.3
(1.1)
43.9
(0.8)
23.5
(0.8)
21.5
(1.0)
Netherlands
9.3
(0.7)
7.1
(0.6)
18.9
(1.0)
22.5
(0.8)
11.1
(0.8)
10.2
(0.7)
New Zealand
6.4
(0.6)
5.6
(0.5)
19.0
(0.8)
23.3
(1.2)
12.0
(0.8)
11.2
(0.7)
Norway
14.9
(0.7)
12.5
(0.8)
18.1
(0.8)
19.0
(1.2)
18.4
(0.8)
15.1
(1.0)
Poland
8.9
(0.7)
9.1
(0.6)
30.5
(0.9)
51.5
(1.1)
10.7
(0.6)
10.2
(0.7)
Portugal
10.8
(0.6)
12.2
(0.7)
36.5
(1.3)
49.2
(1.2)
12.8
(0.7)
15.2
(0.9)
8.1
(0.6)
11.3
(0.8)
22.4
(1.0)
30.9
(1.6)
11.7
(0.8)
15.6
(1.0)
13.4
(0.7)
14.4
(0.7)
29.0
(0.9)
40.8
(1.1)
19.1
(0.6)
22.7
(0.7)
Sweden
6.7
(0.5)
6.0
(0.6)
12.9
(0.8)
15.2
(1.0)
9.1
(0.6)
7.8
(0.5)
Switzerland
7.0
(0.5)
5.2
(0.4)
14.1
(0.7)
16.4
(0.8)
12.7
(0.7)
10.4
(0.6)
26.0
(1.3)
27.4
(1.5)
31.7
(1.2)
36.0
(1.6)
40.9
(1.3)
47.7
(1.6)
7.4
(0.5)
5.7
(0.5)
23.5
(1.1)
26.4
(1.0)
10.7
(0.6)
8.4
(0.5)
United States
14.2
(0.8)
12.0
(0.7)
28.9
(0.9)
35.2
(1.2)
22.6
(1.0)
20.8
(1.0)
OECD average
11.4
(0.1)
10.4
(0.1)
22.7
(0.2)
27.1
(0.2)
17.9
(0.2)
18.0
(0.2)
Argentina
Azerbaijan
8.9
28.0
(0.8)
(1.0)
8.2
24.9
(0.6)
(1.3)
24.0
50.5
(1.5)
(1.1)
33.5
55.1
(1.2)
(1.4)
13.4
37.9
(1.0)
(1.5)
14.0
29.0
(1.0)
(1.3)
Brazil
13.0
(0.8)
13.7
(0.7)
28.8
(0.9)
34.2
(1.2)
21.8
(1.1)
24.1
(1.0)
Bulgaria
18.9
(0.9)
18.3
(1.2)
35.5
(1.5)
46.0
(2.1)
21.3
(1.2)
22.1
(1.1)
Chile
12.7
(0.7)
14.5
(0.7)
25.5
(0.9)
30.8
(1.2)
16.7
(0.8)
18.3
(1.0)
Colombia
15.7
(1.0)
17.4
(1.2)
30.5
(1.3)
38.1
(1.5)
23.3
(1.3)
24.3
(1.0)
Croatia
7.3
(0.6)
7.8
(0.5)
25.3
(1.2)
35.9
(1.3)
14.3
(0.8)
14.9
(0.9)
Estonia
13.9
(0.9)
11.9
(0.8)
24.1
(1.1)
34.4
(1.0)
17.7
(1.3)
17.7
(1.1)
Hong Kong-China
20.2
(1.1)
15.5
(0.9)
30.7
(1.1)
26.5
(1.2)
27.7
(1.1)
27.3
(1.2)
Indonesia
17.0
(1.3)
20.1
(1.3)
27.5
(1.2)
38.3
(1.3)
24.6
(1.6)
28.2
(1.3)
Israel
18.7
(1.1)
17.5
(0.9)
28.0
(1.5)
26.5
(1.2)
43.0
(1.5)
47.5
(1.5)
Jordan
35.6
(1.3)
28.5
(1.1)
50.2
(1.1)
59.7
(1.4)
42.4
(1.3)
36.2
(1.4)
Kyrgyzstan
23.1
(1.0)
27.1
(1.0)
35.2
(1.1)
45.3
(1.1)
26.0
(1.1)
31.1
(1.1)
Latvia
12.0
(0.8)
9.0
(0.8)
29.1
(1.1)
41.7
(1.5)
22.4
(1.3)
22.8
(1.4)
Liechtenstein
9.8
(2.4)
8.2
(2.0)
16.0
(2.7)
13.4
(2.5)
9.7
(2.3)
8.8
(1.7)
Lithuania
8.6
(0.6)
8.6
(0.6)
26.0
(1.2)
38.3
(1.1)
10.5
(0.7)
11.0
(0.8)
Macao-China
18.4
(0.8)
15.9
(1.1)
23.0
(0.9)
26.1
(1.1)
22.4
(1.0)
20.4
(1.0)
Montenegro
19.8
(0.8)
19.5
(0.9)
38.9
(1.2)
47.0
(1.2)
18.1
(0.9)
16.3
(0.8)
Qatar
32.7
(1.0)
27.2
(0.8)
42.6
(1.0)
44.5
(0.9)
42.6
(1.1)
34.4
(0.9)
Romania
18.7
(0.9)
17.2
(0.8)
28.0
(1.2)
31.7
(1.7)
27.6
(1.1)
31.8
(1.1)
Russian Federation
20.3
(1.2)
19.4
(0.8)
53.5
(1.6)
66.0
(1.4)
18.0
(1.1)
18.3
(1.0)
Serbia
14.3
(0.7)
13.7
(0.8)
29.6
(1.3)
40.0
(1.2)
19.1
(0.8)
15.9
(0.8)
Slovenia
11.2
(0.8)
10.8
(0.7)
23.0
(1.0)
33.0
(0.8)
17.8
(0.8)
19.1
(0.8)
Chinese Taipei
18.5
(0.8)
16.0
(0.6)
23.7
(0.8)
21.1
(0.9)
38.6
(1.3)
36.9
(1.4)
Thailand
19.6
(1.2)
18.2
(1.1)
29.1
(1.4)
37.4
(1.2)
18.6
(1.2)
19.0
(1.1)
Tunisia
Uruguay
38.3
9.7
(1.1)
(0.6)
41.2
11.3
(1.2)
(0.7)
50.8
17.4
(1.4)
(0.9)
56.9
27.3
(1.1)
(1.2)
49.7
15.5
(1.1)
(1.0)
50.8
15.5
(1.3)
(0.9)
Denmark
Luxembourg
Mexico
Slovak Republic
Spain
Turkey
United Kingdom
Partners
Self study - Science
Females
Source: OECD PISA 2006 Database.
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
75
Appendix B – dATA TABLES
Table 17 [Part 2/2] Percentage of males and females doing homework for science. mathematics and reading
Self study - Mathematics
Partners
OECD
Males
76
Out of school - Reading
Females
Males
Self study - Reading
Females
Males
Females
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
2 or
more
hours
S.E.
Australia
Austria
34.5
41.2
(0.9)
(1.4)
35.7
46.1
(1.1)
(1.2)
12.5
4.9
(0.5)
(0.6)
11.4
3.1
(0.5)
(0.4)
29.1
24.0
(0.9)
(1.2)
35.3
27.8
(0.8)
(1.3)
Belgium
31.7
(0.9)
41.4
(1.3)
8.0
(0.4)
7.5
(0.5)
17.4
(0.7)
24.8
(0.8)
Canada
33.8
(0.9)
45.7
(0.9)
13.7
(0.5)
17.4
(0.7)
26.6
(0.8)
39.3
(0.8)
Czech Republic
21.0
(1.0)
19.2
(1.0)
8.4
(0.6)
11.2
(0.6)
14.4
(0.9)
18.9
(1.2)
Denmark
32.1
(1.0)
37.5
(1.2)
31.8
(1.1)
41.3
(1.1)
40.3
(1.1)
53.9
(1.4)
Finland
13.8
(0.9)
17.7
(1.0)
4.7
(0.5)
4.7
(0.4)
8.6
(0.7)
17.3
(1.0)
France
28.5
(1.3)
39.2
(1.1)
10.6
(0.7)
15.1
(0.8)
19.5
(0.9)
35.0
(1.0)
Germany
43.0
(1.1)
55.0
(1.3)
12.3
(0.8)
10.3
(0.7)
33.0
(0.8)
41.7
(1.3)
Greece
41.7
(1.3)
41.6
(1.2)
29.5
(1.0)
40.9
(1.0)
32.5
(1.1)
47.2
(1.1)
Hungary
32.1
(1.1)
39.6
(1.4)
21.2
(0.9)
29.5
(1.1)
29.4
(1.0)
43.9
(1.5)
Iceland
30.0
(1.1)
35.9
(1.0)
8.5
(0.7)
5.7
(0.6)
24.4
(0.9)
31.1
(0.9)
Ireland
31.8
(1.2)
36.9
(1.2)
12.0
(0.8)
10.5
(0.7)
28.8
(1.0)
36.8
(1.4)
Italy
49.7
(1.1)
57.2
(1.1)
16.0
(0.6)
15.8
(0.6)
54.3
(1.0)
72.8
(0.8)
Japan
25.1
(1.5)
27.6
(2.0)
7.8
(0.6)
5.8
(0.6)
12.5
(0.9)
12.9
(1.1)
Korea
44.7
(2.0)
47.8
(1.5)
33.9
(1.6)
32.9
(1.7)
22.9
(1.0)
25.7
(1.0)
Luxembourg
28.6
(1.0)
35.5
(0.9)
11.5
(0.7)
8.6
(0.6)
19.2
(0.9)
24.0
(0.8)
Mexico
41.6
(1.0)
45.8
(0.9)
22.9
(1.0)
20.0
(0.9)
36.5
(1.0)
41.4
(1.0)
Netherlands
24.3
(1.2)
28.9
(1.3)
9.6
(0.7)
10.1
(0.8)
17.7
(0.9)
21.2
(1.0)
New Zealand
26.8
(1.1)
30.4
(1.2)
11.3
(0.9)
11.3
(0.6)
23.5
(1.2)
32.7
(1.0)
Norway
22.3
(1.0)
26.0
(1.3)
18.5
(0.8)
21.4
(0.9)
20.9
(0.8)
29.8
(1.4)
Poland
34.5
(1.0)
50.4
(1.0)
9.9
(0.7)
10.1
(0.8)
35.6
(1.2)
57.3
(0.9)
Portugal
33.0
(1.3)
46.7
(1.1)
8.7
(0.7)
9.5
(0.7)
26.1
(1.0)
39.9
(1.3)
Slovak Republic
30.6
(1.3)
37.1
(1.4)
10.4
(0.7)
18.2
(1.0)
27.5
(1.1)
43.6
(1.4)
Spain
34.0
(1.0)
47.1
(1.1)
11.9
(0.7)
10.6
(0.6)
30.7
(1.0)
45.4
(1.0)
Sweden
14.5
(0.8)
15.7
(0.8)
9.2
(0.6)
11.1
(0.7)
14.6
(0.8)
20.5
(1.0)
Switzerland
29.0
(1.0)
34.3
(1.0)
8.1
(0.5)
7.3
(0.5)
19.0
(0.7)
23.4
(0.8)
Turkey
44.2
(1.3)
52.9
(1.8)
33.4
(1.2)
42.7
(1.6)
38.1
(1.2)
51.0
(1.7)
United Kingdom
24.0
(0.8)
26.3
(1.0)
10.3
(0.7)
9.1
(0.6)
25.1
(0.9)
32.2
(1.1)
United States
37.2
(1.1)
45.8
(1.0)
18.3
(1.0)
24.4
(0.8)
30.2
(1.2)
43.3
(1.2)
OECD average
32.0
(0.2)
38.2
(0.2)
14.3
(0.1)
15.9
(0.2)
26.1
(0.2)
35.7
(0.2)
Argentina
Azerbaijan
30.6
57.6
(1.5)
(1.5)
36.4
55.4
(1.6)
(1.5)
8.8
37.8
(0.8)
(1.3)
6.3
34.0
(0.7)
(1.3)
23.8
57.2
(1.5)
(1.1)
32.0
64.3
(1.5)
(1.4)
Brazil
29.0
(1.0)
37.1
(1.1)
20.2
(0.9)
24.4
(0.9)
28.2
(0.9)
34.8
(1.0)
Bulgaria
36.1
(1.6)
46.3
(1.9)
21.4
(1.1)
20.9
(1.2)
35.7
(1.4)
47.7
(1.9)
Chile
29.0
(1.1)
29.4
(1.3)
17.1
(1.0)
19.3
(1.1)
24.5
(1.0)
30.2
(1.3)
Colombia
36.2
(1.3)
37.9
(1.4)
21.2
(1.2)
22.8
(1.2)
30.7
(1.3)
38.0
(1.5)
Croatia
32.2
(1.0)
41.4
(1.2)
8.6
(0.8)
6.4
(0.5)
25.3
(1.0)
35.6
(1.3)
Estonia
32.6
(1.0)
47.3
(1.3)
15.5
(1.0)
16.3
(1.0)
25.7
(1.1)
35.5
(1.3)
Hong Kong-China
43.3
(1.3)
47.9
(1.4)
14.6
(0.9)
13.0
(0.9)
27.1
(1.1)
33.9
(1.1)
Indonesia
33.0
(1.2)
44.4
(1.0)
22.7
(1.5)
22.9
(1.2)
27.3
(1.3)
36.0
(1.0)
Israel
47.0
(1.9)
49.1
(1.7)
25.5
(1.4)
28.0
(1.1)
29.9
(1.5)
33.2
(1.2)
Jordan
52.0
(1.1)
57.6
(1.3)
39.8
(1.6)
36.0
(1.4)
49.8
(1.5)
56.5
(1.2)
Kyrgyzstan
35.2
(1.1)
46.7
(1.3)
28.2
(1.0)
36.4
(1.2)
37.5
(1.1)
51.7
(1.3)
Latvia
45.7
(1.4)
58.4
(1.5)
16.2
(1.0)
13.9
(1.2)
36.6
(1.2)
46.2
(1.3)
Liechtenstein
23.8
(3.7)
26.5
(3.3)
5.2
(1.8)
3.7
(1.2)
12.8
(2.7)
15.8
(2.9)
Lithuania
28.4
(1.1)
41.5
(1.2)
10.5
(0.6)
10.2
(0.7)
28.3
(1.1)
43.6
(1.2)
Macao-China
35.1
(1.1)
46.4
(1.2)
18.6
(1.0)
17.7
(1.0)
22.3
(1.0)
27.6
(1.1)
Montenegro
36.0
(1.1)
38.3
(1.2)
13.4
(0.8)
14.1
(0.8)
32.6
(1.1)
42.3
(1.1)
Qatar
45.9
(1.0)
46.5
(0.9)
35.7
(1.0)
28.4
(0.9)
41.1
(1.0)
39.8
(0.9)
Romania
38.9
(1.3)
43.7
(1.1)
26.7
(1.2)
34.9
(1.4)
36.0
(1.2)
51.9
(1.5)
Russian Federation
40.3
(1.5)
50.4
(1.3)
11.6
(0.6)
11.3
(0.8)
23.4
(1.4)
27.1
(1.0)
Serbia
33.8
(1.1)
38.4
(1.1)
17.6
(0.8)
17.2
(0.8)
28.4
(1.2)
37.2
(1.0)
Slovenia
32.4
(1.0)
43.5
(1.0)
13.2
(0.7)
11.4
(0.7)
22.6
(0.9)
27.8
(1.0)
Chinese Taipei
37.7
(1.2)
40.4
(1.5)
13.0
(0.7)
15.2
(0.6)
30.4
(0.9)
40.7
(1.2)
Thailand
28.6
(1.5)
37.4
(1.2)
13.3
(1.1)
7.8
(0.7)
26.5
(1.4)
32.0
(1.1)
Tunisia
47.7
Uruguay
22.6
Source: OECD PISA 2006 Database.
(1.2)
(1.2)
53.8
30.4
(1.2)
(1.1)
31.8
10.2
(1.2)
(0.9)
38.8
11.9
(1.5)
(0.7)
37.3
16.3
(1.2)
(0.9)
45.9
27.3
(1.5)
(1.1)
Equally prepared for life? How 15-year-old boys and girls perform in school – ISBN 978-92-64-06394-5 – © OECD 2009
Equally prepared for life?
HOW 15-YEAR-OLD BOYS AND GIRLS PERFORM IN SCHOOL
Growing demand has led to the need for a better understanding of the different educational experiences, successes
and eventual outcomes that prevail for men and women world wide.
Compelling moral, social and economic incentives for individuals and societies have motivated research to better
understand gender differences from early childhood through to labour market participation. Research focusing on
gender differences can inform policy endorsing gender equity. More specifically, research on educational performance
and attitudes can be effective in promoting quality student outcomes and equity.
The OECD’s Programme for International Student Assessment (PISA) explores the educational performance and
attitudes of 15-year-old girls and boys. This report begins with a general summary of gender differences measured
outside of the PISA assessment programme. It then considers the knowledge gained about gender-related issues from
previous PISA cycles. Some key findings include:
– In reading in PISA 2000, girls significantly outscored boys in all countries.
– In mathematics in PISA 2003, boys outscored girls somewhat.
– In the combined science scale in PISA 2006, there was no overall significant difference observed between boys and
girls. However, when examining the various science competencies, knowledge components and attitudes to science,
there were some marked differences.
FURTHER READING
The first results from PISA 2006 were published in PISA 2006: Science Competencies for Tomorrow’s World
(OECD, 2007)
THE OECD PROGRAMME FOR INTERNATIONAL STUDENT ASSESMENT (PISA)
PISA is a collaborative process among the 30 member countries of the OECD and nearly 30 partner countries and
economies. It brings together expertise from the participating countries and economies and is steered by their
governments on the basis of shared, policy-driven interests. Its unique features include:
– The literacy approach: PISA defines each assessment area (science, reading and mathematics) not mainly in terms of
mastery of the school curriculum, but in terms of the knowledge and skills needed for full participation in society.
– A long-term commitment: It enables countries to monitor regularly and predictably their progress in meeting key
learning objectives.
– The age-group covered: By assessing 15-year-olds, i.e. young people near the end of their compulsory education,
PISA provides a significant indication of the overall performance of school systems.
– The relevance to lifelong learning: PISA does not limit itself to assessing students’ knowledge and skills but also asks
them to report on their own motivation to learn, their beliefs about themselves and their learning strategies, as well as
on their goals for future study and careers.
The full text of this book is available on line via this link:
www.sourceoecd.org/education/9789264063945
Those with access to all OECD books on line should use this link:
www.sourceoecd.org/9789264063945
SourceOECD is the OECD’s online library of books, periodicals and statistical databases.
For more information about this award-winning service and free trials ask your librarian, or write to us
at [email protected].
www.oecd.org/publishing
www.pisa.oecd.org
ISBN 978-92-64-06394-5
96 2004 12 1 P